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Kinematics of Cricket Phonotaxis

Georgios PetrouT

HE

U N I V E RS

IT

Y

OF

ED I N B U

RG

H

Doctor of Philosophy

Institute of Perception, Action and Behaviour

School of Informatics

University of Edinburgh

2012

Abstract

Male crickets produce a species specific song to attract females which in response

move towards the sound source. This behaviour, termed phonotaxis, has been the sub-

ject of many morphological, neurophysiological and behavioural studies making it one

of the most well studied examples of acoustic communicationin the animal kingdom.

Despite this fact, the precise leg movements during this behaviour is unknown. This

is of specific interest as the cricket’s ears are located on their front legs, meaning that

the perception of the sound input might change as the insect moves. This dissertation

describes a methodology and an analysis that fills this knowledge gap.

I developed a semi-automated tracking system for insect motion based on com-

mercially available high-speed video cameras and freely available software. I used it

to collect detailed three dimensional kinematic information from female crickets per-

forming free walking phonotaxis towards a calling song stimulus. I marked the insect’s

joints with small dots of paint and recorded the movements from underneath with a pair

of cameras following the insect as it walks on the transparent floor of an arena. Track-

ing is done offline, utilizing a kinematic model to constrainthe processing. I obtained,

for the first time, the positions and angles of all joints of all legs and six additional

body joints, synchronised with stance-swing transitions and the sound pattern, at a 300

Hz frame rate.

I then analysed this data based on four categories: The single leg motion analysis

revealed the importance of the thoraco-coxal (ThC) and body joints in the movement

of the insect. Furthermore the inside middle leg’s tibio-tarsal (TiTa) joint was the cen-

tre of the rotation during turning. Certain joints appear to be the most crucial ones for

the transition from straight walking to turning. The leg coordination analysis revealed

the patterns followed during straight walking and turning.Furthermore, some leg com-

binations cannot be explained by current coordination rules. The angles relative to the

active speaker revealed the deviation of the crickets as they followed a meandering

course towards it. The estimation of ears’ input revealed the differences between the

two sides as the insect performed phonotaxis by using a simple algorithm. In general,

the results reveal both similarities and differences with other cricket studies and other

insects such as cockroaches and stick insects.

The work presented herein advances the current knowledge oncricket phonotactic

behaviour and will be used in the further development of models of neural control of

phonotaxis.

iii

AcknowledgementsFirst and foremost, I would like to thank my supervisor Barbara Webb for her guid-

ance, support and encouragement throughout this project. Iwould like to thank her

especially for her patience and understanding during the last months of my studies

and for letting me work on the stick insect robot. I would alsolike to thank my sec-

ond supervisor Berthold Hedwig for valuable comments and helpful suggestions. My

visit to his lab during my first year has inspired many of the ideas presented herein.

I would like to thank the third member of my committee Subramanian Ramamoorthy

for always asking the right questions.

I would like to thank my examiners Jeremy Niven and Taku Komura for their sug-

gestions and comments to improve this thesis.

I would like to thank the Informatics technicians Hugh Cameron, Douglas Howie,

Gilbert Inkster and Robert MacGregor for constructing most parts of the experimental

setup and for all their help to make the rest of my crazy ideas come true. Addition-

ally, I would like to thank Robert for our collaboration on theIntelligent Autonomous

Robotics course and the stick insect project.

I would like to thank John Bender and Ty Hedrick for sharing their tracking soft-

ware. Even though I ended up using neither of them they helpedme improve my own

approach. I would like to thank OpenCV developers and community for such a great

library. I would like to thank Sergey Bochkanov the main developer of ALGLIB for

his help and for sharing his library.

I would like to thank Stefan Schoneich and Mark Payne for showing me how to

prepare the crickets for experiments.

I would like to thank Michael Mangan for proofreading this document.

I would like to thank my office mates, fellow PhD students, IPAB members for

our everyday interactions and especially iPub members for our Friday evening pub

meetings.

On a more personal note, I would like to thank my friends in Edinburgh and back

home for making these years a pleasant experience.

Finally, I would like to thank my sister Julie for taking careof our home while I

was away and my parents Ioannis and Foteini for their unconditional love and support

throughout these years.

My research was funded by the University of Edinburgh, EPSRC, the University

of Edinburgh Development Trust and was supported by the freecoffee machines in the

Informatics Forum.

iv

P.S.I feel that I should apologize for all the horrible things I did to the crickets.

Nevertheless, it was all in the name of science.

v

To my family

vi

“We hope that, when the insects take over the world, they willremember with gratitude

how we took them along on all our picnics.”

–Bill Vaughan

And forget about the experiments we did to them, I would add.

vii

Declaration

I declare that this thesis was composed by myself, that the work contained herein is

my own except where explicitly stated otherwise in the text,and that this work has not

been submitted for any other degree or professional qualification except as specified.

I confirm that the work submitted is my own, except where work which has formed

part of jointly-authored publications has been included. My contribution and the other

authors to this work has been explicitly indicated below. I confirm that appropriate

credit has been given within the thesis where reference has been made to the work of

others.

(Georgios Petrou)

Chapter 3 is based on work from the following jointly-authored publication:

Petrou, G., Webb, B., Detailed tracking of body and leg movements of a freely walk-

ing female cricket during phonotaxis. Journal of Neuroscience Methods, 203(1):56-68.

I conceived, designed and performed the experiments, wrotethe software and analysed

the data. Most of the technical work was done by Hugh Cameron, Douglas Howie,

Gilbert Inkster and Robert MacGregor and the rest by me. I wrote the paper with

Barbara Webb and we corrected it based on recommendations from two anonymous

reviewers.

viii

Table of Contents

1 Introduction 1

1.1 Research Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Background 5

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Cricket Behavioural Ecology . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 Phonotaxis . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.2 Calling Song . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3.1 Motor System . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.2 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.4 Experimental Strategies . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.4.1 Walking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.4.2 Phonotaxis . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5 Behavioural Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.5.1 Walking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.5.2 Walking During Phonotaxis . . . . . . . . . . . . . . . . . . 18

2.6 Neurophysiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.6.1 Nervous System . . . . . . . . . . . . . . . . . . . . . . . . 19

2.6.2 Local and Ascending Thoracic Auditory Neurons . . . . . .. 20

2.6.3 Local Brain Neurons . . . . . . . . . . . . . . . . . . . . . . 21

2.6.4 Descending Brain Neurons . . . . . . . . . . . . . . . . . . . 22

2.6.5 Motor neurons and Walking Interneurons . . . . . . . . . . . 23

2.7 Models and Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.7.1 Walking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.7.2 Phonotaxis . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

ix

2.8 Open Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3 Methodology 33

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.2.1 Arena . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.2.2 Animal Preparation and Experimental Protocol . . . . . .. . 36

3.2.3 Acoustic Stimulation . . . . . . . . . . . . . . . . . . . . . . 37

3.3 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3.2 Kinematic Model (“Skeleton”) . . . . . . . . . . . . . . . . . 38

3.3.3 Tracker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.3.4 Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.3.5 Sound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.3.6 Stance-Swing . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3.3.7 Player . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4 Analysis 59

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.2 Single Leg and Body Angles . . . . . . . . . . . . . . . . . . . . . . 62

4.3 Leg Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.4 Angles Relative to Speaker . . . . . . . . . . . . . . . . . . . . . . . 85

4.5 Ears’ Input Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 92

5 Discussion 103

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.3 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.3.1 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.3.2 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.3.3 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.3.4 Robot Implementation . . . . . . . . . . . . . . . . . . . . . 110

5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Bibliography 111

x

List of Figures

1.1 Female and male cricket . . . . . . . . . . . . . . . . . . . . . . . . 2

2.1 Cricket song elements and properties . . . . . . . . . . . . . . . . .. 8

2.2 Schematic of a female cricket . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Properties of the cricket legs . . . . . . . . . . . . . . . . . . . . . .10

2.4 Auditory system of the cricket . . . . . . . . . . . . . . . . . . . . . 12

2.5 Three common experimental setups . . . . . . . . . . . . . . . . . . 14

2.6 Illustrations of typical leg step, stability and gaits .. . . . . . . . . . 17

2.7 Nervous system and neural pathways . . . . . . . . . . . . . . . . . .20

2.8 Cruse’s rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.1 The experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.2 The kinematic model of the cricket . . . . . . . . . . . . . . . . . . .44

3.3 A screenshot of the tracker module of the software . . . . . .. . . . 46

3.4 The procedure of tracking a point . . . . . . . . . . . . . . . . . . . .47

3.5 A screenshot of the player module of the software . . . . . . .. . . . 48

3.6 Results for the body joint angles . . . . . . . . . . . . . . . . . . . . 50

3.7 Results for the front legs’ joint angles . . . . . . . . . . . . . . .. . 51

3.8 Results for the middle legs’ joint angles . . . . . . . . . . . . . .. . 52

3.9 Results for the hind legs’ joint angles . . . . . . . . . . . . . . . .. . 53

3.10 The stance swing transitions for all the legs . . . . . . . . .. . . . . 54

3.11 The transformed smoothed path and trackball coordinates . . . . . . . 55

3.12 Deviation in tracking acuity . . . . . . . . . . . . . . . . . . . . . .. 57

4.1 Distributions of time properties for all the experiments . . . . . . . . 62

4.2 Leg patterns during forward walking, right turn and leftturn . . . . . 67

4.3 Front right leg’s percentage of angle values . . . . . . . . . .. . . . 70

4.4 Front left leg’s percentage of angle values . . . . . . . . . . .. . . . 71

xi

4.5 Middle right leg’s percentage of angle values . . . . . . . . .. . . . 72

4.6 Middle left leg’s percentage of angle values . . . . . . . . . .. . . . 73

4.7 Hind right leg’s percentage of angle values . . . . . . . . . . .. . . . 74

4.8 Hind left leg’s percentage of angle values . . . . . . . . . . . .. . . 75

4.9 Front right leg’s percentage of body’s angle values . . . .. . . . . . 76

4.10 Front left leg’s percentage of body’s angle values . . . .. . . . . . . 77

4.11 Front right leg joints’ inside and outside turns contributions. . . . . . 79

4.12 Middle right leg joints’ inside and outside turns contributions. . . . . 80

4.13 Hind right leg joints’ inside and outside turns contributions. . . . . . . 81

4.14 Stepping combinations during forward walking, right turn and left turn 83

4.15 Examples of angles between the ears and the speakers . . .. . . . . . 89

4.16 Summary of the angles of interest relative to the speakers . . . . . . . 90

4.17 Angles before change of direction during forward walking . . . . . . 91

4.18 Simulation of sound directionality for a stationary cricket . . . . . . . 96

4.19 Single step cycle decibel difference . . . . . . . . . . . . . . .. . . . 97

4.20 Single step cycle decibel difference . . . . . . . . . . . . . . .. . . . 98

4.21 Examples of ear’s input estimation . . . . . . . . . . . . . . . . .. . 99

4.22 Example of ear’s input estimation during turn . . . . . . . .. . . . . 100

4.23 Decibel values in the angles peaks before change of direction during

forward walking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

xii

List of Tables

3.1 Model joint and segments with parameters and limits . . . .. . . . . 45

4.1 Time properties for all the experiments . . . . . . . . . . . . . .. . . 61

4.2 Step distances covered during swing . . . . . . . . . . . . . . . . .. 64

4.3 Step distances covered during swing in trackball . . . . . .. . . . . . 64

4.4 Number of swings, mean values and standard deviations ofswing du-

ration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.5 Ratio of protraction / retraction . . . . . . . . . . . . . . . . . . . .. 65

4.6 Total number and probability of next steps during forward walking . . 84

4.7 Total number and probability of next steps during right turn . . . . . . 84

4.8 Total number and probability of next steps during left turn . . . . . . 85

4.9 Angles properties for all the experiments . . . . . . . . . . . .. . . . 88

4.10 Average lengths of body and leg segments . . . . . . . . . . . . .. . 93

4.11 Stationary cricket joints and segment parameter values . . . . . . . . 94

4.12 Transmission gains and internal delays of the four sound inputs . . . . 94

xiii

List of Abbreviations

AEP Anterior Extreme Position

AN1 Ascending Neuron 1 of the cricket auditory system

AN2 Ascending Neuron 2 of the cricket auditory system

ASA Active Set Algorithm

CNS Central Nervous System

CoM Centre of Mass

CPG Central Pattern Generator

CS Contralateral spiracle

CSV Comma Separated Values

CT Contralateral tympanum

CTr Coxo-Trochanteral joint

DoF Degrees of Freedom

FL Front left leg

fps frames per second

FR Front right leg

FTi Femoro-Tibial joint

GA Genetic Algorithm

HL Hind left leg

xv

HR Hind right leg

IS Ipsilateral spiracle

IT Ipsilateral tympanum

LED Light Emitting Diode

MDF Medium density fibreboard

ML Middle left leg

MR Middle right leg

ON1 Omega Neuron 1 of the cricket auditory system

ON2 Omega Neuron 2 of the cricket auditory system

PEP Posterior Extreme Position

RMS Root mean square

SD Standard Deviation

SRI Syllable Repetition Interval

ThC Thoraco-Coxal joint

TiTa Tibio-Tarsal joint

TrF Trochanteral-Femoro joint

XML Extensible Markup Language

xvi

Chapter 1

Introduction

Insects combine remarkable locomotion abilities with a variety of exceptional sens-

ing capabilities. Think for example of the speed of a runningcockroach (Delcomyn,

1971), the height and distance a locust jumps (Bennet-Clark, 1975), the moth’s sense

of smell (Kennedy and Marsh, 1974) and the visual response offlies (Autrum, 1958).

With less than one million neurons (compared to billions of neurons in mammals) in-

sects exhibit a wide variety of behaviours such as communication (Von Frisch, 1967),

nest building (Franks et al., 1992) and prey pursuit (Olberget al., 2000). Their com-

paratively small nervous system allows the identification of specific neurons related to

a behaviour and model entire circuits. Additionally, they have more stereotyped be-

haviours than vertebrates, making experiments easier to reproduce (Maye et al., 2007).

Finally, techniques such as amputations can be employed, which are considered uneth-

ical on other animals.

All these features have attracted the attention of scientists from different fields such

as biologists and engineers. Some insects are more specialised in particular behaviours

and therefore are preferred for the relevant studies. For instance, ants and bees in navi-

gation (Wehner, 2003; Srinivasan et al., 2000), crickets for sound communication (Hu-

ber and Thorson, 1985), cockroaches and stick insects for walking (Mu and Ritzmann,

2005; Cruse and Bartling, 1995) and locusts for jumping (Heitler and Burrows, 1977).

Studies usually focus either on the sensor processing or themotor output. But how

does an insect convert a sensory input to motor output in order to produce a specific

behaviour? And how does the motor output affect the sensor input in return? These

two fundamental questions are the main motivation behind this study. To address these

questions I conducted a behavioural investigation on the acoustic communication of

crickets (figure 1.1), in which the precise details of leg andbody motion in response to

1

2 Chapter 1. Introduction

sound were tracked.

It has been almost a century since Regen (1913) by using a telephone, proved that

the auditory communication between a male and a female cricket is independent of

visual, olfactory and tactile stimulus. Female crickets are able to recognise the singing

patterns of conspecific males and move towards them. This behaviour is one of the

most well studied processes of auditory communication in the animal kingdom, in-

cluding behavioural experimentse.g. Bailey and Thomson (1977), neuron recordings

e.g. Boyan (1980) or bothe.g. Staudacher and Schildberger (1998). Most research

has focused on the sound processing and the walking direction but not the movements

of the legs. Yet leg movement is doubly interesting in this behaviour, not only as

the mechanism by which directional changes are actuated, but also as the location of

the cricket’s auditory organs on their forelegs, the perception of sound is directly in-

fluenced by walking. Current information available about theleg movements during

phonotaxis is limited to the description of the walking patterns (Murphey and Zaret-

sky, 1972), limited joint movement information (Baden and Hedwig, 2008) and tarsi

positions (Witney and Hedwig, 2011).

Figure 1.1: Female (left) and male (right) cricket. Photo taken by Hugh Pastoll.

1.1 Research Aims

Current information on cricket’s leg movements during phonotaxis is far from com-

plete. The purpose of this work is to contribute towards the understanding of the in-

teraction between auditory perception and motor action in crickets and the phonotactic

1.2. Thesis Outline 3

behaviour in general. More specifically the goals of this dissertation are:

• To conduct behavioural experiments on female crickets while they perform free

walking phonotaxis and obtain more detailed kinematic information. For this

purpose a new tracking methodology is devised, implementedand assessed.

• Analyse the data obtained for the individual joint, leg and body movements of

the insect. Compare the results with previous studies and other insects.

• Estimate the auditory input that the insect receives duringphonotaxis.

1.2 Thesis Outline

The rest of this dissertation is divided into four chapters:

Chapter 2 surveys the current literature by incorporating the current knowledge on

the morphology, methodology, neurophysiology and modelling aspects of phonotactic

and walking behaviour, mainly for the crickets but also other insects.

Chapter 3 describes the methodology followed to investigatethe auditory-motor

interaction. A new type of arena was created along with custom made circuitry aug-

menting commercially available high speed cameras.

Chapter 4 presents an analysis based on collected data following the proposed

methodology. The analysis covers joint, leg and body movements, leg coordination,

angles relative to the sound source and an estimation of the ears input and the effect on

the movements of the insects.

Chapter 5 concludes the thesis by summarizing the results andpresenting future

work aspects regarding improvements on the methodology, further experimentation

and modelling of the behaviour in simulations and robotic platforms.

Chapter 2

Background

2.1 Introduction

In the previous chapter, I briefly introduced the subject andthe goals of this disser-

tation. In this chapter, I review the relevant background information to this study,

with a particular focus on the cricket literature. When necessary, I will incorporate

knowledge from other insects, especially regarding walking. I begin by introducing

the cricket species I will concentrate on, its key features and explain basic concepts of

phonotactic behaviour including information about the song used for communication

(section 2.2). Then, I describe the basic morphology of the insect incorporating its

motor system and various sensors, including its auditory system (section 2.3). Next,

I present the different experimental strategies used to study walking and phonotaxis

(section 2.4) and I include the current knowledge of the aforementioned behaviours

(section 2.5). Then, I summarize the basic neurophysiologyinformation with respect

to the location and function of the neurons (section 2.6). Next, I refer to models and

robots based on insect walking and phonotaxis (section 2.7). Finally, I conclude this

chapter by summarising the open questions derived from the current literature (section

2.8).

2.2 Cricket Behavioural Ecology

Crickets belong to the order of Orthopteran insects, which also encompasses katydids,

grasshoppers and locusts. There are more than 2600 species,many of them nocturnal,

living in a variety of habitats (Walker and Masaki, 1989). For instance, field crickets

live on the ground, under rocks and burrows and mole cricketslive underground in

5

6 Chapter 2. Background

tunnels that they construct. In contrast to locusts they prefer to walk rather than jump

and despite the fact that they have wings, most of them are notable to fly.

Many species of Orthoptera, including crickets, have the ability to generate sounds

by rubbing together certain hard body parts. This process ofsound production is

termed stridulation. Male crickets produce sounds by rubbing their two forewings

which contain rows of corrugated bumps. Only few cricket species have been investi-

gated in depth. Most of them belong to the families of field crickets, bush crickets and

mole crickets.

Adult females of the speciesGryllus bimacullatus(de Geer), which belongs to the

family of the field crickets, will be used in this study.Gryllus bimacullatus, from now

on referred to simply as the cricket, unless otherwise specified, has been the subject

of numerous behavioural and neural studies related to auditory communication (Popov

and Shuvalov, 1977; Popov et al., 1978; Selverston et al., 1985; Huber and Thorson,

1985; Stabel et al., 1989; Wendler, 1990; Michelsen et al., 1994; Poulet and Hedwig,

2005). There is also a significant amount of literature regarding behavioural and neural

data associated with walking (Laurent and Richard, 1986a,b;Bohm and Schildberger,

1992; Nishino, 2003; Witney and Hedwig, 2011), making this species a suitable organ-

ism to investigate the auditory and walking behaviour interaction.

2.2.1 Phonotaxis

Taxis is a behaviour that involves the responsive movement of an organism towards

or away from an external stimulus source (Fraenkel and Gunn,1940). Organisms that

have a paired set of sensors and move towards the most strongly stimulated side pro-

duce positive taxis. If they turn towards the less stimulated sensor they produce nega-

tive taxis. Organisms that have only one sensor, can still produce taxes by turning left

and right and measuring the different stimulus intensities. Taxes depend on a frequent

signal from the stimulus source, but they are robust compared to accurately identifying

the source and planning a path towards or away from it and alsocan deal with changes

in the position and orientation of the source. Examples of taxes include phototaxis

which involves movement in response to light stimulation, chemotaxis which involves

movement in response to chemical stimulation and thermotaxis which involves move-

ment in reponse to temprerature gradient.

The ability of an organism to approach a sound source is called positive phonotaxis

or simply phonotaxis. Female crickets approach to the male calling song is one of the

2.2. Cricket Behavioural Ecology 7

most well studied behaviours in acoustic communication andit requires pattern recog-

nition and sound localisation. Many studies have focused onaspects of this behaviour.

For example, the nervous system’s generation and control ofthe song (Kutsch and Hu-

ber, 1989), the physics of sound production (Bennet-Clark, 1989) and the tracking of

the song by the female (Weber and Thorson, 1989).

2.2.2 Calling Song

Males of most species can produce more than one type of song. These are mainly the

calling song, which is used to attract the females from a longdistance, a courtship song

which is used when the female is in a close proximity (when other kind of cues such

as tactile, vision and chemosensory stimuli play an important role) and an aggressive

song used to establish territory and signal possible combatwith other males. The

calling song is the most studied and the one of interest to this study.

A typical pattern of the calling song is illustrated in figure2.1(a). It consists of short

sound pulses called syllables (figure 2.1(c)). Each syllable is produced when the male

closes its forewings, is around 16–20 ms long and has a frequency between 4.5–5.0

kHz. A group of syllables separated by a short pause is calleda chirp (figure 2.1(b)).

Every chirp contains 3–5 syllables and has 350-400 ms duration (Huber, 1960), al-

though there are significant differences between individuals of about 100 ms (Doherty,

1985). The syllable repetition interval (SRI) within a chirpis around 45 ms (Doherty,

1985). A chirp that has no pauses between syllables and continues for a prolonged

period is called a trill. Environmental factors such as temperature affect the production

of sound and therefore properties of the song. The temperature for the aforementioned

properties values was between 20-21◦C. Doherty (1985) found that the syllable period

and the chirp period are affected by temperature changes. Incontrast, the number of

syllables, the syllable period and the carrier frequency were relatively unaffected.

Crickets do not live isolated in their environments. Sounds from other insects and

other species are present in their daily lives. In order to beable to successfully locate

the males of their species, the females must recognise the unique properties of their

calling song. Some of the parameters of the song are crucial for this process. The

syllable period has been found to be the most important (Thorson et al., 1982). Other

parameters such as the syllable duration, the chirp interval and the number of syllables

contribute to the attractiveness of the song (Popov and Shuvalov, 1977; Doherty, 1985;

Stout and McGhee, 1988).


EEEEE

ZZZ

ZZ

(a) Song elements

EEEEE

cccc

cc

(b) Chirp properties

(c) Syllable properties

Figure 2.1: Cricket song example with its elements and properties. (a) Calling song

consists of syllables and chirps. (b) A chirp is composed by one or more syllables and

is defined by its duration and period. (c) A syllable has certain carrier frequency and is

defined by its duration and period.

2.3 Morphology

An adult cricket is between 25-30 mm in length. As in all insects, a sclerotized cover

called the exoskeleton, supports the body by surrounding all the soft tissue. The cricket

has a laterally compressed cylindrical body shape (figure 2.2), composed of three main

sections: head, thorax and abdomen. The thorax consists of three regions: prothorax

(front), mesothorax (middle) and metathorax (hind). Each of these parts has a pair of

legs. The rear legs are much larger than the front and middle,allowing the insect to

kick and jump. Despite their differences in size and function, each leg is composed of

the five following segments: coxa, which is attached to the thorax, trochanter, femur,

tibia and tarsus (figure2.3(a)). The femur is the largest segment in mass and length

in any of the legs. The tarsus is further separated in severalsegments, connected by

passive joints and a claw, which make it very flexible. It is used to grasp objects and

provide feedback about ground contact.

2.3. Morphology 9

Figure 2.2: Schematic of a female cricket with main parts identified. Insects have ser-

sors such as compound eyes, antennae and cerci to gather information from the envi-

ronment. Additionally to the males, the females have an ovipositor to lay eggs in the

soil.

2.3.1 Motor System

Each segment is moved by one or more pairs of antagonistic muscle groups, located in

the previous leg segment (figure 2.3(c)). Every one of these muscles is attached on one

side to a cuticular ingrowth (apodeme) and the other to the exoskeleton. The thoraco-

coxal (ThC) joint is a 3 degrees of freedom (DoF) connection, controlled by three pairs

of muscle groups (promotor-remotor, abductor-reductor and anterior-posterior rotator).

The front leg ThC joints have larger range of motion than the other regions, allowing

the insect to perform actions such as cleaning the eyes and the antennae (Laurent and

Richard, 1986a). The coxo-trochanteral (CTr - controlled by levator-depressor muscle

groups), trochanteral-femoro (TrF), femoro-tibial (FTi -controlled by extensor-flexor

muscle groups) and tibio-tarsal (TiTa) joints have 1 DOF each in every leg. As in

most insects, the TrF joint has little movement and therefore coxo-trochanteral-femoro

is considered as one joint. The coxal segments of the front legs are moving almost

vertical relative to the ground, while the coxae of the hind legs are moving almost

parallel to the ground and the coxae of the middle legs are moving somewhere in

between (see figure 2.2).

2.3.2 Sensors

In order to monitor the state of their body and appendages andobtain information from

their environment, insects have various types of mechanosensors. Positionally, they

are distinguished into cuticular mechanoreceptors that are situated on the exosceleton


(a) (b)

(c)

Figure 2.3: Properties of the cricket legs. (a) Front leg with segments identified. (b)

Front leg with the main joints and their angular variables. The tarsus segments are not

included. (c) Femur muscles and apodemes of the metathoracic leg.

and internal mechanoreceptors that are located inside the exosceleton, mostly near

articulations. Functionally, they are separated into exteroceptors which are sensitive to

outside stimuli such as air flow or touch and proprioceptors which respond to internal

movements such as joint movement. The latter can be positionsensors such as hair

plates and chordotonal organs; and load sensors such as campaniform sensillae, strand

and tension receptors.

2.3.2.1 Mechanosensors Involved in Walking

Various mechanosensors involved in walking have been identified in parts of the main

body and legs of the crickets. One dorsal hair plate is located in each trochanter

(Gnatzy and Hustert, 1989) and is used to estimate the angle between two joints. The

chordotonal organs sense velocity, acceleration and position in each joint (stick insect;

Hofmann et al., 1985). They have been studied for their neural morphology (Nishino

and Sakai, 1997; Nishino, 2000) and function (Acheta domesticus; Nowel et al., 1995).

In total 30 of them are located in the thorax. The campaniformsensilla measure the

increase and decrease of the forces caused by the motion of the legs. There are 4-5

groups of them, in each Trochanter (Gnatzy and Hustert, 1989) and a group of 14-15

2.3. Morphology 11

in each Tibia (Eibl, 1978). The strand receptors function quite similarly to the chor-

dotonal organs. The tension receptors are located in some ofthe muscles and signal

the force generated by the muscle. Crickets have also gravityreceptors (Horn and

Bischof, 1983; Horn and Foller, 1985). All these sensors can act in parallel and possi-

bly influence each other (stick insect; Cruse et al., 1984). For a detailed table of sensor

distribution in crickets see (Gnatzy and Hustert, 1989).

2.3.2.2 Auditory System

The ears are the most well studied sensors of the cricket. They evolved from proprio-

ceptive chordotonal organs (Boyan, 1998) linked to a pair of tympanic membranes, on

each foreleg, placed in the upper part of the tibia (figure 2.4(a)). Each pair consists of

a large tympanum on the back of the leg and a smaller and less important tympanum

on the front (Larsen, 1987). Tracheal tubes connect tympanito each other and to a

pair of spiracles located in the front of the body, forming anH-shaped internal struc-

ture. A double central membrane separates the two sides (themedial septum). The

lower branches of this structure end at the tympani and the upper branches end in the

spiracles. For a complete description of the structure of those organs see (Ball et al.,

1989).

The difference in the amplitude of the sound signal in the left and the right tympani

is very low, because of the small distance of the ears (∼1.5 cm) relative to the wave-

length of the sound signal (∼7 cm) and the distance of the sound source. Besides the

direct route, where the sound waves reach each tympanum fromthe outside, there is a

second indirect route thought the spiracles. As a result, the sound waves can pressure

each tympanum both from inside and outside (Huber and Thorson, 1985), making the

ear a pressure-difference receiver (Michelsen et al. 1994,Carew 2000, figure 2.4(b)).

The two tympani will have a difference in the amplitude of their summed signals (from

the four inputs), relative to the signal frequency, sound direction and diffraction. Con-

sequently, the female, turns towards the side with the loudest sound and moves towards

the sound source. For a detailed description of the sound perception in crickets see

(Larsen et al., 1989).


(a) (b)

Figure 2.4: Auditory system of the cricket. (a) Position of the auditory system parts in

the body. (b) Sound transmission through the tympani and the spiracles.

2.4 Experimental Strategies

Several experimental setups have been used, to study walking and phonotactic be-

haviour. They are different in the information they provideand the constraints they

impose on the insect. Usually, a setup that has more constraints insect provides more

information about the auditory input and the motor output.

2.4.1 Walking

Walking behaviour in the absence of sound stimulation in crickets has been studied

using a treadmill (Acheta domesticus; Laksanacharoen et al., 2000) (figure 2.5(a)).

The cricket walks on the transparent belt with a mirror at 45◦ below it, with small dots

of paint applied at its leg joints and body on one side. This enables a simultaneous

side and bottom view of the insect and the digitizing of the marked points, leading to

a 3D reconstruction of the legs movements. Information about the angles of the joints

were obtained by using inverse kinematics (Laksanacharoenet al., 2003). This setup

allowed the kinematic analysis of straight walking but it cannot be used to study turning

or a combination with sound stimulation. Another method is anarrow corridor with

mirrors which was used to study the motion ofGryllotalpa orientalis(Zhang et al.,

2011). This method has similar constraints to the treadmill. Also, because the insect is

seen from the top and the side not all the joints are visible and therefore simplifications

have to be made. An arena has been used as a less constrained setup, to study the

forces generated by each leg but did not provide informationabout the movements of

2.4. Experimental Strategies 13

each joint (Harris and Ghiradella, 1980).

For other insects, data has been obtained for turning behaviours during free walk-

ing, e.g., for ants (Zollikofer, 1994), bees (Zolotov et al., 1975), cockroaches (Franklin

et al., 1981; Camhi and Levy, 1988; Jindrich and Full, 1999), flies (Strauss and Heisen-

berg, 1990; Mason et al., 2005), and stick insects (Cruse, 1976; Rosano and Webb,

2007). However, these generally report only the tarsus positions or foot-touchdown

locations and the body orientation; in some cases forces exerted by the legs were also

measured. More detailed three dimensional kinematic data has sometimes been ob-

tained for free walking insects,e.g. for stick insects (Durr, 2001) and cockroaches

(Kram et al., 1997; Watson et al., 2002) but these are usuallyin situations where the

animal is restricted from turning (the insect is walking on abeam or treadmill, or

in a channel). These studies also required hand-digitisation to extract the joint posi-

tions from every frame. Joint angles have been estimated by using inverse kinematics

calculations (Cruse and Bartling, 1995). Comparable kinematic detail that includes

turning responses has otherwise been obtained only using animals that are restricted

by tethering above a trackball or a slippery surface,e.g., in beetles (Frantsevich and

Mokrushov, 1980), cockroaches (Bell and Kramer, 1979; Nye and Ritzmann, 1992;

Mu and Ritzmann, 2005; Ridgel et al., 2007; Bender et al., 2010),and stick insects

(Durr and Ebeling, 2005; Gruhn et al., 2009). In this type of study, some methods have

been developed for at least partially automating the extraction of data from high speed

videos. This usually involves marking of the joints,e.g., with reflective paint (Larsen

et al., 1995). Commercial motion capture systems such as WinAnalyze (Mikromak,

Erlangen, Germany) have been used with such markers (Gruhn et al., 2006). Most

recently Bender et al. (2010) have used image filtering and brightest point detection

within a region near the expected marker location in an automated tracking algorithm

to follow 26 marked points on cockroach legs, using two high-speed cameras to obtain

three dimensional position data. A method that is widely used in human motion track-

ing is to constrain the tracking problem by defining a kinematic model that is fitted

to the tracked points in the image (Aggarwal and Cai, 2002). This approach has been

successfully applied to tracking a stick insect by Zakotniket al. (2004). In fact, this

approach is particularly appropriate, as instead of treating the the problem as one of

tracking an arbitrary set of points (raw joint positions) inspace, it assumes the points

belong to a specific kind of articulated body. Indeed, the control problem for the insect

is to use its muscles to change the angle(s) of each joint, in acoordinated fashion that

propels it in a desired direction; thus to analyse the kinematics it is more useful to


know the angle than the position of each joint.

2.4.2 Phonotaxis

Field studies have rarely been used to investigate cricket phonotaxis, due to the dif-

ficulty of obtaining detailed and accurate data from an animal most active after dark

and moving on the ground. Experiments including laboratorysetups have been per-

formed outside to take advantage of realistic environmental conditions (Romer, 1993;

Kostarakos and Romer, 2010).

Various types of arenas have previously been used includingrectangular arenas

(Scapsipedus marginatus; Murphey and Zaretsky, 1972),(Acheta domesticus; Stout

et al., 1983) a circular arena (Teleogryllus oceanicus; Bailey and Thomson, 1977), a

Y-Maze (Rheinlaender and Blatgen, 1982), a Y-maze globe (Hoy and Paul, 1973) and

a sound proof box (Payne, 2010). These setups can provide thepath of the insect dur-

ing phonotaxis, but it is not possible to determine the exactauditory input at a specific

time. They are also useful in performing choice tests such assimultaneously presenting

two songs and check the cricket’s preference (Popov and Shuvalov, 1977; Pollack and

Hoy, 1979). It is also possible to extract information aboutthe orientation and speed of

the insects (Scapsipedus marginatus; Murphey and Zaretsky 1972,Teleogryllus ocean-

icus; Bailey and Thomson 1977,Plebeiogryllus guttiventris; Mhatre and Balakrishnan

2007).

(a) Transparent treadmill (b) Kramer treadmill (c) Trackball

Figure 2.5: Three common experimental setups.

Setups that provide more information require that the insect is more restricted such

as a Kramer treadmill (Weber et al., 1981) (figure 2.5(b)), a paired tread wheel (Stabel

et al., 1989) and a trackball (Baden and Hedwig, 2008) (figure 2.5(c)). In the Kramer

2.5. Behavioural Studies 15

treadmill the cricket is placed on top of a plastic sphere, with a small disk of reflective

foil attached to her back. An infrared photodetector from the top senses the location of

the insect and corrects the forward-backward and left-right position of the sphere, plac-

ing the cricket on the top. Although this setup does not recreate the exact conditions of

the insect’s natural environment, it allows the reconstruction of the insect path as if it

had walked on the ground. In the trackball setup the cricket is attached by a restraining

arm, which holds it on the top of an air-suspended sphere. An optical sensor then de-

tects the left-right and forward-backward movements of theball. This setup allows the

measurement of the insect turning tendencies and has much faster time resolution than

the Kramer treadmill. More recently it was used to obtain information of tarsi, head

and abdomen positions (Witney and Hedwig, 2011). Some of these methods impose

constraints on the insect movement which may make either tracking or interpretation

of body and leg positions difficult. For instance, the insect’s dynamics are altered if

it is fixed on top of a trackball: propelling its own weight forward is not equivalent

to propelling a ball backward (Poulet and Hedwig, 2005). Additionally, the spatial

relation of the insect to external stimuli is held constant,which, whilst’ providing suit-

able experimental control, does not reflect the normal phonotaxis situation. Some leg

segments may not be visible from the available views of an animal on a trackball.

2.5 Behavioural Studies

2.5.1 Walking

Walking behaviour in insects has been mostly studied for thecockroach (Tryba and

Ritzmann, 2000), the stick insect (Cruse, 1976; Epstein and Graham, 1983; Bassler and

Buschges, 1998) and the locust (Burrows, 1996b). Walking is a task that requires for

its maintenance translation of parameters, such as direction and velocity into actions

and overcoming or avoiding obstacles. Typically, the control of walking in insects can

be divided in two main tasks: The control of the movement of the single leg and the

coordination of all the legs.

In order to produce successful movements, each joint in every leg, has to be in

harmony with the movement of the other joints in the same leg.For the crickets, the

ThC and CTr joints mainly determine the mobility of the whole leg and the amplitude

of the step, while the FTi and TiTa joints allow an increase ofthe arc determined by the

tarsus (Laurent and Richard, 1986a). As a result of the differences in the morphology


of the crickets legs, in each pair of legs the segments are moved in a different way than

the others.

Thoracic differences have been noted in other insects (cockroach; Watson and Ritz-

mann, 1997), (locust; Burns, 1973). The stepping cycle of thesingle leg consists of two

phases: stance (power stroke) and swing (return stroke) (figure 2.6(a)). Stance is the

phase when the leg is touching the ground, supports the body and pushes it forward.

Swing is the phase when the leg is lifted off the ground and moves forward until it

reaches a reliable foothold on the ground. These two phases have major differences in

their control requirements. During swing the leg does not require mechanical coupling

with the other legs until it reaches the ground and thereforecontrol is simpler, while in

stance there is mutual mechanical coupling through the ground with the other legs, so

as to support the body. Consequently, the transition events between the two phases are

critical for the successful movement of the leg. The anterior extreme position (AEP)

is where the leg touches the ground and the posterior extremeposition (PEP) is where

the leg lifts off the ground. The two phases and critical positions have been extensively

studied for the stick insect (Cruse, 1985a,b).

The coordination of all six legs is essential for the successful movement of the body.

Therefore, each one of them needs to communicate with at minimum the neighbouring

ipsilateral and contralateral legs, so that they produce a stable gait. To have a statically

stable gait, the centre of mass (CoM) must be within the polygon spanned by the legs

on the ground (figure 2.6(b)). If the CoM projects outside of the stability polygon, the

body is pulled by gravity and the insect falls. Insects typically walk utilising a tripod

(used for high speed, with three legs touching the ground) ormetachronal (used for

slow speed and at least four legs touch the ground) gait (figure 2.6(c)). In the tripod

gait, the front and rear leg of one side and the middle leg of the other side, perform

their swing movements at the same time, while the other threelegs support the animal.

In the metachronal gait there is a sequence of stance-swing transitions on ipsilateral

legs that is not in phase with contralateral legs. Cockroaches exhibit different walking

speeds: A slow speed(<10 cm/sec) and a faster (∼30 cm/sec) when tested on an arena

(Bender et al., 2011). According to Harris and Ghiradella (1980) crickets of the species

Acheta domesticus, have gaits similar to cockroaches, with tripod gaits at high speeds

and metachronal gaits at lower speeds.

Insects can turn in various degrees (cockroach; Comer and Dowd, 1987), on the

spot (Simmons, 1990), while walking (cockroach; Watson andRitzmann, 1997) or

while running (cockroach; Jindrich and Full, 1999). Duringwalking, turning can be

2.5. Behavioural Studies 17

(a) (b)

(c)

Figure 2.6: Illustrations of typical leg step, stability and gaits. (a) The stepping cycle is

divided in stance or power stroke (solid line) and swing or return stoke (dashed line).

The transitions between the two phases are the anterior extreme position (AEP) and the

posterior extreme position (PEP). The arrow shows the direction of the leg during the

stepping cycle. (b) The black legs are touching the ground, forming a stability polygon

(in this case a triangle), while the white legs are moving forward. The centre of mass

is located between the middle and hind legs. When it is within the stability polygon the

insect is statically stable. (c) In the metachronal gait at least four legs are on the ground

at any time and diagonal pair of legs are stepping approximately together (marked with

an ellipse). In the tripod gait the front and rear legs on one side and the middle leg on

the other side are stepping together. The black marks indicate the swing phase and the

white the stance phase.

achieved by increasing step frequency (Graham, 1972) or step length (Strauss and

Heisenberg, 1990). The initiation of turning in insects canbe activated by brain neu-


rons (Ridgel et al., 2007) or reflex pathways that bypass the brain (Camhi and Johnson,

1999).

The small size of the leg segments makes it extremely difficult to study all the

degrees of freedom in insects. Consequently most experiments focus on three DoF.

The ThC which moves the leg forward and backwards, the CTr which moves the femur

up and down and the FTi which moves the tibia closer or furtherfrom the femur.

Laksanacharoen et al. (2000) analysed the forward walking on a treadmill ofAcheta

domesticus. In this research the complex movement of the coxae was determined for all

the thoracic segments, indicating that only the front ThC joints use more than 1 DoF.

There are obvious differences in the movements of the legs that belong to different

thoracic segments. For instance the hind legs move almost vertical relative to the

ground, while the front legs extend far forward.

2.5.2 Walking During Phonotaxis

Crickets approach the sound source in a series of consecutiveruns and pauses follow-

ing a meandering path (Weber and Thorson, 1989). The existing knowledge on phono-

tactic walking involves mostly turning tendencies measurements, such as direction,

pathway and translational and rotational velocity (Weber et al., 1981; Schildberger,

1988; Stabel et al., 1989; Doherty, 1991).

Murphey and Zaretsky (1972) found that there is a correlation between the walk-

ing bouts and the stops inScapsipedus marginatus. This however might be because the

calling song of this species occurs less that one per second.Instead,Gryllus campestris

had number of stops independent of the presence of the calling song (Schmitz et al.,

1982). Earlier studies suggested that turns are followed bya stop and that the rest

of the walking is not affected by the sound direction (Murphey and ZaretskyScap-

sipedus marginatus; 1972, Bailey and ThomsonTeleogryllus oceanicus; 1977). More

recent studies on different species suggest that changes inwalking angles occur during

walking (Plebeiogryllus guttiventris; Mhatre and Balakrishnan, 2007).

Earlier experiments on a Kramer treadmill showed that the crickets were deviating

by 30◦-60◦ with respect to the animal’s frontal midline. Recently, the accuracy of the

directionality of the cricket was tested with the sound source present±30◦ in front of

the insect (Schoneich and Hedwig, 2010). It was found that it could move towards the

correct direction even if the sound was placed 1◦ relative to the cricket’s length axis.

Hedwig and Poulet (2004), using a highly sensitive trackball system, were able to

2.6. Neurophysiology 19

measure more precisely the movements of the females. The results showed that they

make rapid steering movements in response to each sound pulse of a communication

signal, independent of the species specific song. This observation indicates that phono-

tactic turning is initiated by a combination of reactive movements and brain neurons

commands. Furthermore, Baden and Hedwig (2008) recorded themovements of the

front legs using the same experimental setup. The up-down movements of the leg were

not altered by the sound direction. In contrast, the left-right movements were clearly

dependent on the sound direction, making larger movements towards the contralateral

speaker and smaller towards the ipsilateral speaker.

Witney and Hedwig (2011) used one camera to record cricket movements from

the top using the same experimental setup. This provided mainly information about

the foot positions of each leg but due to the setup limitations I discussed previously,

this did not allow precise information about each joint contribution and especially the

ThC joints. The front and middle legs on both sides adjusted their movements during

turning, but the altering of the hind legs’ movements was small compared to forward

walking.

2.6 Neurophysiology

In the next paragraphs, I present the different groups of neurons that participate in

the phonotactic turning behaviour and their contribution.The summary begins with

the overall structure of the nervous system and continues with the local and ascending

thoracic auditory neurons, the local brain neurons, the descending brain neurons and

the motor neurons and the walking interneurons. The terminology for the neurons

presented herein is not uniform, so I will include the names given by the authors in the

citations.

2.6.1 Nervous System

The central nervous system (CNS) of insects is composed of a series of ganglia (a col-

lection of neurons), which are linked by intersegmental connectives, allowing signals

to travel up and down this chain (figure 2.7(a)). The front ganglion is the brain, fol-

lowed by the subesophageal ganglion, three thoracic ganglia and finally several abdom-

inal ganglia. The highest concentration of neurons is located in the brain (∼300K neu-

ron cells in crickets (Schildberger et al., 1989), 100 timesgreater than any of the other


ganglia), which is responsible for learning (Matsumoto andMizunami, 2002), process-

ing of visual, olfactory (Schildberger, 1984a) and antennal (Gebhardt and Honegger,

2001) input and the overall control of the behaviours. The role of the subesophageal

ganglion in locomotion is unclear, although evidence suggests it participates in main-

taining normal walking (Altman and Kien, 1987). A thoracic ganglion is located in

each one of the pro-, meso- and metathoracic segments and controls the muscles of

the front, middle and hind legs respectively, as well as processing sensory input from

these segments including auditory.

(a)

(b)

Figure 2.7: Nervous system and neural pathways. (a) Position of the ganglia in the

cricket body. (b) Basic information flow between different regions of the body.

2.6.2 Local and Ascending Thoracic Auditory Neurons

There are∼60 primary auditory afferents in each auditory organ (Michel, 1974). Their

axons project to the auditory neuropil in the prothoracic ganglion (Eibl, 1978), trans-

mitting information to local and ascending thoracic interneurons. Two pairs of ascend-

ing auditory neurons are well characterized (Gryllus campestris, AN1 AN2; Wohlers

and Huber, 1982), (HF1AN; Popov and Markovich, 1982), (Schildberger, 1988; Schild-

berger and Horner, 1988), (Hennig, 1988,Teleogryllus commodus; STU, LAU), (Stabel

et al., 1989), (TH1-AC1, TH1-AC2 Zorovic and Hedwig, 2011). Each AN1 receives


excitatory input from the ear contralateral to the cell body(Horseman and Huber,

1994). The AN1 pair is tuned to the calling song frequency andits hyperpolariza-

tion leads to the change of walking direction. Each AN2 receives input from both ears.

The AN2 pair responds to higher frequencies and evidence suggest that is involved

in bat avoidance (Schildberger, 1984b). Other identified neurons in the prothoracic

ganglion in different cricket species are the descending neuron DN1 which receives

excitatory input from the contralateral ear and responds tothe calling song like AN1

neurons; and a T-shaped neuron TN1 which receives excitatory input from both ears

(Wohlers and Huber, 1982,Gryllus campestris). However, the functional role of these

neurons during phonotaxis is not clearly characterized.

All the identified auditory cell types in the thorax have their bodies located within

the ganglion and each cell type has a mirror image (Wohlers and Huber, 1978). There

are two well known bilateral pairs of mutually inhibitory omega neurons (Selverston

et al., 1985), participating in phonotactic behaviour (Teleogryllus Oceanicus, Interneu-

rons 1 and 2; Casaday and Hoy, 1977), (Wohlers and Huber, 1982,Gryllus campestris,

ON1 ON2;), (LSAN; Popov and Markovich, 1982), (Wiese, 1981). ON1 is sharply

tuned to the frequency of the song. Some studies suggest thatthese neurons do not

participate in the temporal filtering of the song pattern (Wohlers and Huber, 1982;

Schildberger et al., 1989), while others had evidence for low-level temporal filtering

(Wiese and Eilts, 1985; Stabel et al., 1989). More recently,(Nabatiyan et al., 2003;

Baden and Hedwig, 2007), it was proposed that the ON1 acts as a low-pass filter for

the syllable patterns and that its instantaneous spike ratematches the tuning of phono-

tactic behaviour. Furthermore, the sound localization is activated independent of pat-

tern recognition. Therefore, the pattern recognition is not directly involved in the rapid

steering responses (Poulet and Hedwig, 2005).

2.6.3 Local Brain Neurons

Some local brain neurons have been associated with the recognition of the calling

song. Schildberger identified two neuron classes with auditory responses (Schild-

berger, 1984b). The first, BNC1 gets direct input from the thoracic AN1 neurons

(examined in the next section). Then it provides input to thesecond class the BNC2.

The neurons belonging to both classes have different responses to the pattern of sound,

acting as low- and high- pass filters. Similar neurons have been identified in other

studies (UABN, PABN1, PABN2; Boyan, 1980), (Acheta domesticus, HBB1; Atkins


et al., 1988). Additionally, Bohm and Schildberger (1992) mention one local neuron

that responded to the calling song with a latency of 25-30ms,but they do not clarify if

it is one of the already identified neurons. This neuron acteddifferently in the standing

and walking animal. When standing, often only the first syllable elicited spikes, while

in walking there was a response to each syllable.

Schildberger’s theory that high and low pass filtering result in band pass selectivity

for syllable rate remains the most popularly accepted modelfor the recognition of the

calling song. Other theories have been proposed that involve template matching (Hoy,

1978) or cross correlation analysis (Teleogryllus; Hennig, 2003). However, Hedwig

(2006) suggests that these models require at least two pulses of the song syllables

to produce turning. This fact indicates that these models are valid as a recognition

mechanism but are too slow to be directly involved with phonotactic steering.

2.6.4 Descending Brain Neurons

The contribution of the brain to the walking behaviour through descending neurons is

not entirely clear. Decapitated insects respond to external stimulation, but do not pro-

duce the same coordinated movements as they normally do (Zill, 1986, cockroach;).

According to Staudacher (1998), there are about 200 pairs ofdescending neurons to-

wards the thoracic motor centres. This number is small relatively to the total number of

brain cells. The initiation of some behaviours has been associated with these neurons,

for instance in cricket singing (Hedwig, 2000) and stridulation (grasshopper; Hedwig,

1994). For the walking behaviour, one pair of neurons was found (Bohm and Schild-

berger, 1992), that fitted the description of a command neuron. A command neuron can

trigger a complex sequence of neural activity, such as stridulation or walking (Kupfer-

mann and Weiss, 1978). The specific neuron had weak response to auditory, visual

and tactile stimulation, but when walking began it increased its discharge rate. Addi-

tionally, when it was activated the cricket began walking during the discharge of the

neuron.

Furthermore, there are some other descending neurons that have been associated

with the walking and the phonotactic response. In older studies, groups of cells were

active and directly correlated to a parameter of walking, such as translational (Bohm

and Schildberger, 1992) and rotational (Staudacher and Schildberger, 1998) velocity.

Moreover, in (Bohm and Schildberger, 1992) two neurons were found that responded

to auditory stimulation. One of them was a unimodal neuron that exhibited sensitivity


to the direction of the sound. Besides sound stimulation, theother neuron responded

to visual input. However this neuron only responded in the beginning of the calling

song and almost stopped responding after a few chirps. Boyan and Williams (1981)

found two neurons (IDBN and CDBN) that responded to auditory stimulation. The

IDBN neuron is believed to belong to cluster i5 (Staudacher and Schildberger, 1998).

Staudacher (2001) found that the majority of the descendingneurons belonging to

Group II had various responses to calling songs of 5 and 20 kHz.

Based on the above results from crickets and studies in locusts (Kien, 1983, 1990a,b),

Heinrich (2002) proposed that the brain is not only responsible for the initiation and

maintenance of walking, but there is a population of descending neurons controlling

parameters of walking behaviour. Each neuron is responsible for a specific subtask,

such as intra- and interleg coordination and contributes with fine tuned adjustments

of walking patterns. This agrees with Baden and Hedwig (2008)who suggest that

the steering commands are integrated with the networks thatcontrol for walking, by

modulating the amplitude of the steering response.

2.6.5 Motor neurons and Walking Interneurons

The ganglia control leg muscles through excitatory and inhibitory motor neurons (Nishino,

2003), (locust; Watson et al., 1985; Siegler and Pousman, 1990), (cockroach; Cohen

and Jacklet, 1967). The number of motor neurons in each ganglion is relatively small.

For example, in the cricket prothorax, coxa, trochanter andfemur in each leg are con-

trolled by 50 motor neurons (1 unpaired and 49 paired) (Laurent and Richard, 1986a).

Each thoracic ganglion generates rhythmic motor patterns which alternate the con-

traction of antagonistic muscles, producing movements of the segments of the individ-

ual leg (locust; Ryckebusch and Laurent, 1993; Burrows, 1996a). The neural networks

that generate these rhythmic patterns are described as “central pattern generators”

(CPGs). Rhythmic motor patterns can be generated without sensory input (cockroach;

Delcomyn, 1980). In stick insects, evidence suggest that they have separate pattern

generator for each joint (Bassler, 1993; Buschges, 1995; Bassler and Buschges, 1998).

The coordination of the leg joints is accomplished through interactions of the modules

that control the joints. However, other results in the stickinsects show that leg joints

do not have strict coordinated motion (Cruse and Bartling, 1995), but are coupled by

sensory information.

In insects, spiking and nonspiking interneurons are responsible for the control of


motor neurons. The difference between the two categories isthat spiking neurons gen-

erate action potentials in order to transmit information (“digital” transmission), while

nonspiking neurons function without generating action potentials (“analog” transmis-

sion). Spiking interneurons main role is to receive and distribute information from

sensory neurons (stick insect; Hess and Buschges, 1999) and possibly contribute to the

control of leg movement (locust; Wolf and Laurent, 1994). For instance, they process

information from the femoral chordotonal organ (fCO) (stickinsect; Buchges, 1994),

(locust; Burrows, 1987). Instead, the nonspiking interneurons main responsibilities are

to receive input from the spiking neurons, control the relevant motor neurons (cock-

roach; Pearson and Fourtner, 1975), (stick insect; Busghes, 1990), (locust; Burrows,

1980; Laurent and Burrows, 1989), posture (locust; Siegler,1981) and modulate the

gain of leg reflexes (stick insect; Buschges et al., 1994). The sensory information plays

a greater role in insects that move slowly (i.e. stick insect) than the ones that move in

greater speeds (i.e. cockroach) (Delcomyn, 2004).

Besides the processing of information in each thoracic ganglion, the communica-

tion between the segments of the CNS is also important for the control of locomotion,

through intersegmental interneurons (cockroach; Pearsonand Iles, 1973). This com-

munication is essential for the coordination of the movements of all the legs. The

influences between ipsilateral legs are stronger than between contralateral legs (stick

insect; Brunn and Dean, 1994). Additionally, the shared information between con-

tralateral legs differs between the three thoracic ganglia(stick insect; Dean, 1989).

Experiments in reduced leg preparations show that leg coordination is affected by the

interaction of interleg and intraleg sensory feedback (Ludwar et al., 2005; Borgmann

et al., 2007).

Baden and Hedwig (2008) performed recordings of the front legs tibial motor neu-

rons activity during phonotaxis, revealing that the auditory input is indirectly integrated

with the walking networks. There are currently two hypotheses for this connection. Ei-

ther there are local thoracic interneurons connecting the thoracic auditory interneurons

to motor neurons or that there is a connection through the ascending and descending

brain neurons. Therefore, besides the modulation by the legsensors, walking can be

modulated by the auditory response.

2.7. Models and Robots 25

2.7 Models and Robots

2.7.1 Walking

Most insect-inspired hexapod models are based on either thestick insect (Carausius

morosus) or the cockroach (Periplaneta americana, Blaberus discoidalis). Therefore

this section will contain more information about these insects and not crickets. There

are two main categories of controllers: reflex-based and pattern-based. The former

use the sensory input to produce leg movements and coordination, while the latter use

pre-programmed patterns (mostly CPG), which might be modulated by sensor input.

As a result of extensive behavioural and neural experimentson stick insects, Cruse

et al. (1991) proposed a set of rules for leg coordination (figure 2.8) and a “step pattern

generator” for the single leg, capable of changing between stance and swing using

load, position and velocity signals. The rules are the following:

1. The swing movement of a posterior leg inhibits the swing movement of an ante-

rior leg.

2. The start of stance movement of a posterior leg excites theswing movement of

an anterior leg.

3. A backward shift of an anterior leg’s PEP will create an earlier swing movement

and a forward shift of the posterior leg.

4. The posterior leg swing is targeted towards the position of the anterior leg.

5. Increased resistance increases force and increased loadprolongs stance

6. The posterior leg will briefly step backwards when it treads on the anterior leg.

The proposed rules can be applied to forward walking situations. However, during

crossing large gaps or turning the rules have to be adjusted (Blasing and Cruse, 2004;

Durr, 2005). This model produced and maintained stick-insect-like tetrapod and tri-

pod gaits in a simple kinematics simulation. A later version(Cruse et al., 1995), was

based on a neural network architecture and transitioned between stance and swing us-

ing threshold values. The networks were trained with data from real insects. Cruse

et al. (1998) proposed the successor of these models, WalkNet. This network consists

of several subnetworks, of which the most important are the swing-net and the stance-

net controlling swing and stance respectively and the selector-net which controls which


of the two networks is in control of the leg. There are three DoF per leg (α, β, γ). The

network outputs velocities for each joint. A more recent version of the WalkNet model

(Schilling et al., 2007) can function with leg amputations and the selector net is modi-

fied to accept an analogue signal for the load of the leg. This model has been tested in a

dynamic simulation. Although these rules are based on stickinsect locomotion, some

of them or their variations could be applied to other insectssince they have similar

gait patterns. However, insects like cockroaches, locustsand crickets have also greater

differences between the three leg pairs and higher speeds relative to the stick insect.

Figure 2.8: Cruse’s rules that describe the information exchanged between neighbour-

ing legs and the stance-swing transitions they generate.

A different solution regarding the control of stance in stick insects is the idea that

the insect could use the elastic properties of each joint as apositive feedback (Bassler,

1988). Schneider et al. (2005) investigated this approach by using a local positive ve-

locity feedback (LPVF) which can switch from positive feedback to compliant motion

depending on the mechanical power produced by the joint (positive or negative). This

model was tested on a two joint manipulator and a dynamic simulation of a leg Schnei-

der et al. (2006). Rosano and Webb (2007) used LPVF principle in a model for turning,

introducing the thoracic differences observed in the insect.

Although there are indications of central oscillators, even in each leg joint (Bassler,

1988), Walknet does not use any central oscillators to produce rhythmic motor output.

Each bi-stable circuit flexes or extends the associated joint. Cruse (2002) proposed

a simple network for the movement of each joint, where there are two antagonistic

muscles channels of sensor input and motor output. Buschges et al. (1995) and Hess


and Buschges (1997) investigated which sensory signals could affect each of the three

joint oscillators. This means that instead of using simple pattern generators, the joints

movements are influenced by input from their neighbouring joints such as load and

angle information. Akay et al. (2004) proposed a reflex chainfor the generation of the

forward stepping pattern of the middle leg, using three non-centrally coupled oscilla-

tors which are being coordinated by sensory signals. This model has been tested in a

dynamic simulation (Ekeberg et al., 2004) and produced coordinated stepping move-

ments of a middle leg and with some modifications the movements of a front and hind

leg.

Some of the robots mentioned in the upcoming discussion havethe primary goal

of investigating biological systems, while others aim to improve robot performance.

Since all the underlying mechanisms are not fully understood for any insect, neuro-

logical and physiological data are combined with engineering techniques for the con-

struction of robots. Furthermore, the existing robot components are bigger, heavier,

slower, have less power and consume a lot of energy. In contrast to the nervous sys-

tem of an insect that does parallel computations, computerstypically work serially.

Consequently, the walking performance of robots is expectedto be worse than the real

insects.

Most hexapod robots, apart from the fact that they use legs, are not based on biolog-

ical principles,e.g. Yoneda and Ota (2003); Barai and Nonami (2007). Furthermore,

there are robots that are based on insect walking but do not use similar leg structure

or maintain a mechanical coupling for the intra- and inter-leg coordination. Some ex-

amples include a cricket microrobot (Birch et al., 2000), RHex(Saranli et al., 2001),

Whegs (Allen et al., 2003) and MechaRoach (Boggess et al., 2004).

Although WalkNet has only been tested in simulations, the decentralised archi-

tecture and some of Cruse’s rules for leg coordination have been successfully imple-

mented in robots with 3 DoF per leg (Espenschied et al., 1996;Lewinger and Quinn,

2008). The rules are used to adjust each leg’s position at which is lifted off the ground

(PEP) based on the positions of its neighbouring legs. When a leg reaches its PEP it

is lifted off the ground and moves forward. When it reaches itsAEP it moves down

and returns to its stance phase. The PEP of each leg is calculated at each time step.

There are three major mechanisms, that correspond to coordination rules 1, 2 and 5.

The first mechanism makes a leg that is in swing phase preventing the other leg from

swinging by repositioning backwards the PEP of that leg by a small value. The second

mechanism makes a leg that just entered its stance phase encouraging the other leg to


enter its swing phase by moving the PEP of that leg forward by asmall value. The

third mechanism makes a leg that is in stance phase encouragethe other leg to enter its

swing phase by moving the PEP forward by an increasing value.A genetic algorithm

(GA) is usually used for tuning the parameters of the coordination procedure.

The TUM robot (Weidemann et al., 1994) used a single leg controller (SLC) and

a global leg coordination module (LCM), where each leg notified its neighbours of its

state. A mathematical model (Weidemann et al., 1993), divided into four states based

on state machines was used to control the individual leg. This robot was able to gen-

erate trajectories similar to the stick insect whilst avoiding obstacles. Tarry II used an

inverse kinematics model to train a neural network to produce stepping patterns (Frik

et al., 1998). Robot II was also able to create a continuous range of insect-like gaits

(Espenschied et al., 1996). It was able to have reflexes such as searching for foothold,

stepping reflexes, levator reflexes. Rough terrain navigation is accomplished by inte-

grating insect-like reflexes with the gait controller. An elevator reflex is implemented

to surmount large objects, and a searching reflex allows the robot to find a foot support

on uneven or missing terrain. LAURON used a learning approach (Berns et al., 1994)

with coordination signals such as velocity (Ilg and Berns, 1995) to perform insect gaits.

A later version of this robot, LAURON III (Gassmann et al., 2001) used pre-calculated

leg trajectories and replaced the coordination rules with acentralized architecture.

The development of stick-insect-inspired robots focuses on the leg movements and

their coordination, while the cockroach-inspired robots aim towards the morphology

of the insect and its speed. Robots such as sprawlita (Cham et al., 2002) do not have

an insect-like structure but use kinematic features of a cockroach and incorporates

the spring-loaded inverted pendulum leg movements of the insect into its walking,

achieving great speeds. There are also robots the follow predefined trajectories based

on kinematic data from the real insect such as protobot (Delcomyn and Nelson, 2000).

Quinn and colleagues have constructed a series of robots based on the cockroach.

Initially they developed a distributed neural network (Beeret al., 1989) for the control

of walking based on Pearson’s pacemaker model (Pearson and Iles, 1973). Robot I

(Beer et al., 1992) had 2 DoF on each leg and used this neural network as a controller.

A CPG was used to generate the stance and swing transitions foreach leg. It was able

to walk in a straight line, on flat terrain and generate a rangeof cockroach gaits. Robot

III (Quinn and Ritzmann, 1998) is a hexapod with kinematics based on studies of the

Blaberus discoidalis(Watson and Ritzmann, 1997). It has a total of 24 degrees of

freedom with 5 for each front leg, 4 for the middle legs and 3 for the rear legs moved


by air cylinders. The robot uses a distributed hierarchicalcontroller. Robot IV used

passive stiffness joints to store energy during a step cycle. Robot V (Choi et al., 2005)

is able to control not only joint position, but also joint stiffness. These robots are able

to imitate the cockroach posture but not to walk yet, as they are dependent on external

power and air supply.

Some robots are great examples of incorporating the leg structure of real insects

into a robot (cockroach; Delcomyn and Nelson, 2000; Choi et al., 2005), (stick insect;

Pfeiffer et al., 1995). Despite the fact that the robots thatuse artificial muscles are

closer to the biological muscles, they are difficult to control. Furthermore, they rely on

external resources that complicates their performance.

Although robots such as TUM use a decentralised architecture for the coordination

of their legs, each individual leg follows specific trajectories. Additionally, most of the

aforementioned robots do not turn at all or they do not make turns similar to an insect.

Finally, they do not implement the thoracic differences observed in the real insect.

Most robots use sensors related to walking, such as ground orload sensors, but

do not use environmental sensors found on a real insect. For instance, Tarry II uses

an ultrasound sensor for navigation. An antenna tactile sensor mounted on a robot

(Cowan et al., 2005) is a good example of how turning is guided by external sensory

cues. However, this sensor is mounted on a sprawl robot that cannot move the leg

segments in the same manner as the cockroach.

Following the work of (Ekeberg et al., 2004), Lewinger et al.(2006) implemented

the idea of sensory coupled oscillators on a single leg and two front legs. Additional

sensory pathways for elevator reflex (step over a raised obstacle) and searching re-

flex (step over a gap) that are biologically plausible were added on a hexapod robot

(Lewinger and Quinn, 2009). There is also a neural network implementation on an-

other robotic platform that only deals with forward walking(von Twickel et al., 2011,

2012). More recently, a 4 DoF robotic leg based on cockroach middle leg could transi-

tion from forward walking to inside and outside turning by altering the sensory effect

pathways (Rutter et al., 2011).

2.7.2 Phonotaxis

Webb and colleagues have developed successive robots whichmodel the phonotactic

behaviour of crickets, using Schildberger’s (Schildberger, 1984b) low- and high- pass

filter approach. The first model (Webb, 1995) was implementedon a LEGO robot. The


algorithm passes the auditory input from both sides througha phase cancelling process,

lateralizing the sound of the species song frequency. Whenever the ear input value of

one side reaches a threshold value, a turn in the appropriatedirection is initiated. The

first robot was only capable of reacting to slow syllable rates and therefore the model

was transferred to a Khepera robot (Lund et al., 1997), capable of executing the model

at cricket speed. The next model (Webb and Scutt, 2000) reimplemented the first model

using spiking neurons. The most recent model (Reeve and Webb,2003) approaches

the internal structure of the insect neurophysiology, by including some of the identified

auditory neurons (ON1, AN1, BNC1, BNC2) and the connections between them, as

described in section 2.6. All the models were tested in a lab environment. The last

model was used in outdoor experiments, using a Whegs robot (Horchler et al., 2004)

with the ears mounted in the front part. Although the size of the robot prevented

comparisons with the real crickets, it could reproduce the sound localisation of the

insect when tested in a noisy environment, show preference for the species pattern of

the song and distinct between competing sound sources.

In both robot implementations the distance between the two ears is fixed, while

in the real cricket the distance between them relative to theears changes while the

insect moves. None of the aforementioned models has ever been tested on a multi-

segmented legged robot, which could solve the Whegs sharp turns problem. Webb

(2006) proposed a new modification to the current model inspired by the the more

recent experiments on phonotaxis (Poulet and Hedwig, 2005). The key modification is

a direct connection from the AN1 neurons to the motor control, which is modulated by

the BN1 and BN2 neurons. This model has yet to be tested on a robot.

2.8 Open Questions

The current literature review reveals the lack of information in several aspects of the

phonotactic behaviour and is summarized in the following questions:

From section 2.4:

• Can there be an alternative methodology to investigate the phonotactic behaviour,

that allows detailed information about the body and leg movements of the cricket

along with precise auditory input to be obtained while the insect is freely walk-

ing?

From section 2.5:

2.8. Open Questions 31

• What is the motion of each thoracic segment and body part during forward walk-

ing and turning?

• What is the motion of each individual leg joint during forwardwalking and turn-

ing?

• What are the similarities and differences with other insects?

• How do the legs coordinate during straight walking and turning?

• What is the speed of the cricket’s response?

• Are the turns proportional to the sound direction?

• What is the accuracy of the sound tracking and what deviation causes correc-

tions?

From section 2.6:

• What information might the cricket need to modulate its walking?

• What input might the motor neurons need?

From section 2.7:

• Does the cricket data resemble control mechanisms suggested for other insects

for straight walking and turning?

In order to address these questions, I created a new methodology inspired by the

limitations provided in section 2.4 and presented in the following chapter. The method

will allow detailed data about the movements of a female cricket to be gathered while

it performs phonotaxis. Further analysis presented in chapter 4, will allow the investi-

gation of the remaining questions posed above.

Chapter 3

Methodology

N.B.The data presented in this chapter also appears in (Petrou and Webb, 2012).

3.1 Introduction

I concluded the previous chapter by indicating the open questions derived from the

current literature. To address these questions I developeda method which is presented

in this chapter. The overall aim of this method is to track themovements of a female

cricket as it walks on a flat surface during orientation to sound under free walking con-

ditions. It is necessary to film from underneath because all the joints are only visible

from below the insect. Additionally, because of the insect’s speed the videos need to

be high-speed and two cameras have to be used that will allow 3D reconstruction of the

joints. Finally, the information about the joint movementsneeds to be synchronized

with the sound pattern.

The method I describe here generates joint angle data directly by incorporating an

optimization algorithm which fits a kinematic model of the insect to the detected joint

marker positions of each frame. This in turn supports more effective automation of

tracking of the marker positions, building on the image processing methods described

in Bradski and Kaehler (2008), Hedrick (2008) and Bender et al.(2010). This combi-

nation of methods enables tracking a cricket in a new experimental setup in which the

animal can walk freely. The floor of the arena is transparent and thus I can film the

animal from below, using two cameras to obtain three dimensional information about

all leg joints. Instead of using a slippery surface, which alters the forces, or a treadmill

in which the ground moves to keep the animal in the camera image, I instead move

the cameras to follow the animal. The video is then processedoffline by software that

33

34 Chapter 3. Methodology

automates the identification and tracking of joint markers,and reconstructs the motion.

I demonstrate the system by tracking all leg joints and six body joints, synchronised

with the sound pattern, for a cricket initiating a turn to sound from a standing start and

walking towards it.

First I describe the materials and the methods used during the experiments includ-

ing the arena, the preparation of the crickets, the experimental protocol and the acoustic

stimulation (section 3.2). Then I present the software and its modules I developed to

extract useful information from the video sequences (section 3.3). Finally, I present

results from one experiment and evaluate the method (section 3.4).

3.2 Materials and methods

3.2.1 Arena

The setup (figure 3.1) consisted of a wooden (MDF) rectangular box with a transparent

bottom made of acrylic plexiglass (3mm thick). The floor was slightly scratched with

a knife providing friction while the insect walks on it, but preventing significant loss of

image information during camera recordings. The walls and the ceiling were covered

with a sound absorbent material (Audio 90/125 Illtec, Illsonic, Illbruck, Germany) to

reduce sound reflections. Four fluorescent bulbs (75 W, Philips, Holland) provided the

necessary light for the high speed recordings. I found that fluorescent light produces

less flickering than ordinary bulbs, which is crucial for thetracking algorithms. Two

speakers (Samsung SMS-2200), positioned on the left and right side of the box, were

used to attract the insects, by alternately playing the calling song. The sound output

was controlled by a custom made circuit equipped with a switch to change the direction

of the song. In synchrony with the sound pattern this circuitflashed one of two LEDs,

located under the arena and not visible to the animal, depending on the sound direction

and status.

Two high speed colour cameras (Exilim EX-F1, Casio Co. Ltd., Tokyo, Japan;

used with aperture 2.7, shutter speed 1/1000 s, ISO 1600, zoom 36 mm lens, focus

(distance to animal) approximately 10 cm, resolution 512 X 384 pixels) were located

under the plexiglass floor. Each imaged the same floor area of approximately 10 cm

X 7 cm (a cricket covers an area around 4 X 2 cm) providing an effective resolution

of about 5 pixels/mm, and capturing both the movements of theinsect and the LED

signal corresponding to the sound pulses. It was not possible with the cameras used

3.2. Materials and methods 35

to obtain direct synchronisation between them, so the LED onset was also used to

synchronise the cameras. Thus, recording at 300 fps , there could be a maximum

difference of 3.33 ms between the left and right frames. In this worst case scenario,

i.e., the largest possible time gap between frames, I estimate from my data that the

fastest moving point on the cricket (the hind leg tarsus during swing) would change

position by no more than 1mm. I also verified that the potential delay does not add

significant inaccuracy in practice by selecting marked points from the left frame and

checking that the epipolar line in the right frame was over the corresponding point in

the right frame. This was done after the calibration procedure described in section

3.3.1.

The relative position of the cameras was fixed using an aluminium triangle frame-

work (35◦ degrees angle in the top of the triangle and 5 cm distance of the centre of the

cameras lenses from the transparent floor). The framework was placed on two rails,

one of them moving in the X-axis and the other in the Y-axis, and was moved by hand

to maintain the insect near the centre of the images as it walked in the arena. The

rails prevented rotation and translation around the Z-axisof the local coordinate sys-

tem, which simplified subsequent analysis. They also constrained the motion to slow

and steady movement—from the subsequent tracking I could calculate that movement

of the cameras almost never exceeded 0.2 m/s which is the speed at which the image

would be moved more than one pixel within one exposure at a shutter speed of 1/1000

s; consequently, this camera motion did not create significant blur. In order to construct

the global arena position from the local image position I marked the floor with a grid

of points, with 5 cm distance between neighbouring points. The points closest to the

edge of the arena were marked with a number which indicates the global position of

that point. Only one grid point is required for each frame to obtain the global position

as further explained in section 3.3.4.

The cameras produce RGB video files compressed in H.264 format. These were

first edited in Avidemux version 2.5 (http://fixounet.free.fr/avidemux/) to select the

frames of interest: from when the insect starts moving untilit hits the end of its tether

(see below) and the camera is moved towards the edge to find thecorresponding grid

number. I also changed the video container (from QuickTime to AVI) and rotated the

frames to represent left and right images for simplicity, before passing to the tracking

software described in 3.3.


(a)

(b) (c)

Figure 3.1: (a) The experimental setup with (b) top and (c) side schematics. Numbers in

(a) indicate (1) The left speaker position. (2) One of the fluorescent bulbs. (3) The circuit

and the switch that control the sound direction and LEDs. (4) Examples of planar and

non planar calibration objects. (5) The stereo camera system. (6) The LEDs mounted

on a thick metallic wire.

3.2.2 Animal Preparation and Experimental Protocol

Adult intact female crickets of the speciesGryllus bimaculatus(de Geer) were ob-

tained from a local supplier and separated into individual plastic cages, isolated from

the sound of male crickets, before their final moult, maintained on a 12h:12h L:D

photocycle and fed with dried dog food and water. Heat and light were provided by or-

dinary incandescent light bulbs. The experiments were performed at room temperature

(20 - 24◦C), and generally took place around the end of the light periodor beginning

of the dark period of the photocycle when the insect is most active (Loher et al., 1993).

Their age was between one and four weeks after the final moult.Prior to experiments,

3.2. Materials and methods 37

each insect was cold anaesthetized at 4-5◦C for approximately 15 minutes and then

placed on a block of Plasticine by restraining all legs with metal clamps. The two

top wings were removed and a light string was attached to the insect’s back, verti-

cally relative to the body, at the third thoracic tergite using a mixture of wax and resin

(50%−50%). The string was used as a tether to keep the animal in a restricted area in

the arena, so that it could not climb the walls. Small dots (approximately 1mm diam-

eter) of yellow paint (TexPen, Dykem, KS, USA) were applied on the leg joints and at

the centre of the thoracic segments.

The cricket was placed in the arena and the string attached tothe centre of the arena

ceiling. I gave the insect at least 1 hour to adjust to the new environment. A small twist

applied to the string when attaching it meant that it acted asa very soft spring, adopting

a helical shape to incorporate any additional length, and did not touch the ground or

interfere with the animal’s limbs. The recordings were started when the insect had

paused for some time near the middle of the arena, where the string is not stretched

and therefore did not affect its movements. In the example presented in section 3.4,

the sound was switched on from one speaker, and the cricket’smovements followed

until it reached the limit of its tether, after walking about35 centimetres. In most of

my recorded paths the insect first rotated on the spot in orderto orient itself relative to

the sound source and then walked almost directly towards thespeaker, usually without

stopping.

3.2.3 Acoustic Stimulation

An artificial calling song was used, modelled on maleGryllus bimaculatusat car-

rier frequency of 4.8 kHz, syllable duration of 21 ms including 2 ms rise and fall

time, syllable period of 42 ms, chirp duration of 252 ms and chirp period of 500 ms

(Thorson et al., 1982), generated by a MATLAB 7.7 (Mathworks, Natick, MA, USA)

script at 44.1 kHz sampling rate. The calling song was playedback by using Audacity

(http://audacity.sourceforge.net/) and presented by PC audio boards via the two active

speakers. Sound intensities were calibrated to 75± 1 dB at the centre of the arena

by using a sound level meter CEM DT-805 (Shenzhen Everbest Machinery Industry

Company Ltd., Shenzhen, China) angled towards the active speaker.


3.3 Software

The nature of my method requires the processing of a few thousand frames per exper-

iment. In order to avoid the manual digitization of multiplepoints in every frame, I

developed software to assist me in my effort.

The software package called CricketTracker was developed inC++.NET, using Vi-

sual C++ Express (Microsoft, Redmond, WA). The package integrates a GUI with

the OpenCV library version 2.2 (http://opencv.willowgarage.com), which I use for

the video processing as it offers many built-in image processing functions that I use

in my application. Most of the vision methods used in my system are described

in Bradski and Kaehler (2008). Furthermore, the software utilizes basic OpenGL

(http://www.opengl.org/) functions for depicting the 3D reconstruction of the tracked

data. My software includes modules for camera calibration,kinematic model defi-

nition, tracking of marked joints, tracking of grid points,tracking sound status and

direction, stance - swing transitions and playback. All thedifferent modules of the

software are explained in the following sections.

3.3.1 Calibration

The calibration module is used to calibrate the stereo camera system, using the Stere-

oCalibrate algorithm in OpenCV. The calibration can use either a planar chessboard or

a shape of known geometry and dimensions. In my case I used an object composed

of LEGOTMbuilding blocks (Lego, Denmark) marked with 60 points as thecalibration

object. The average RMS reconstruction error was 0.3586 millimetres, and average

RMS projection error 0.7856 pixels. Note this calibration takes into account cam-

era distortion, by fitting a polynomial transformation, andtherefore I can undistort

the video frames. During video recordings, the insect is usually near the centre of the

frames, but the grid points are sometimes near the edges where there is more distortion.

3.3.2 Kinematic Model (“Skeleton”)

In this module the user can define the kinematic model that will be used during the

tracking procedure. This consists of a definition of the joints and the segments, the

number of axes through which each segment can be rotated and thus the number of

angles defined for each joint, and the minimum and maximum possible values for each

of these angles (an angle can also be fixed at a constant value). The model has an

3.3. Software 39

initial (root) point that defines the overall translation and rotation of the model relative

to the axes origin, as it is fitted to the data. Limits for the permitted offset in x, y and z

position of this initial point during the fitting procedure can also be specified; typically

I used limits of±0.5 mm. The rotation order of the angles can also be selected here.

In my case I used

R= Ry×Rx×Rz (3.1)

The module saves the defined joint and segment information inXML files. Anexample of the two files with one element is provided in the next lines.

<?xml version="1.0" encoding="utf-8"?>

<Joints>

<Joint Name="Middle Right Femur Tibia" IsRoot="False" IsLegRoot="False" />

</Joints>

<?xml version="1.0" encoding="utf-8"?>

<Segments RotationOrder="2" RootOffset="0.5" OppositeSegmentsSameLength="True">

<Segment Name="Middle Left Tibia" FromJoint="8" ToJoint="9" OppositeSegment="4" HasAlpha="False"

IsAlphaConstant="False" MinAlpha="-180" MaxAlpha="180" HasBeta="True" IsBetaConstant="False"

MinBeta="0" MaxBeta="180" HasGamma="False" IsGammaConstant="False" MinGamma="-180" MaxGamma="180"/>

</Segments>

As an example, see figure 3.2(a), 3.2(b) and table 3.1 for the definition of the model

used for tracking the cricket. Detailed anatomy of the cricket leg joints has not (to

my knowledge) been published except for the proximal front leg joints (Laurent and

Richard, 1986a); a much briefer description of the degrees offreedom (DoF) for all leg

joints is given in (Laksanacharoen et al., 2000). My model was obtained by making

reasonable assumptions based on these and other insect studies, and also by system-

atically exploring the effect on tracking accuracy of increasing or decreasing the DoF

of various joints. A body model that lacks a true DOF of the cricket will produce

greater fitting errors, whereas an unnecessary DOF in the model will not significantly

improve the fitting errors, and can be discounted. For example, I found that including

constant rotation in the trochanter-femur joint for the front legs (a similar rotation of

the corresponding joint is assumed in recent cockroach studies Bender et al. (2010))

significantly improved the fit, but adding this rotation or a DoF to this joint in the other

legs made little difference.

The model used consists of three central joints in the pro-, meso- and meta-thorax

parts of the thorax. The root point of the translation and rotation of the kinematic model

is set to the meta-thorax joint which is close to the centre ofmass and provides better

matching values during the tracking procedure than any other body joint. The meso-

thorax is considered to have the same rotations as the meta-thorax, but the prothorax


has three DoF. The rest of the joints were introduced in chapter 2 in section 2.3.1. The

TiTa joint is marked but the rotation of the tarsus is not estimated; and a single marker

is tracked for the CTr and TrF joints, which jointly act to rotate the femur relative to

the coxa. The front legs (figure 3.2(e)) have three DoF in the ThC joint (Laurent and

Richard, 1986a), a one DoF hinge CTr joint, a constant 45◦ rotation of the TrF joint,

and a one DoF hinge FTi joint. The middle legs (figure 3.2(d)) have three DoF in the

ThC joint and one DoF hinge CTr and FTi joints; the TrF joint does not move. The

hind legs (figure 3.2(c)) similarly have three DoF in the ThC joint, a one DoF hinge

CTr joint and a one DoF hinge FTi joint.

Once the model has been defined, it needs to be initiated by theuser manually

selecting a point corresponding to each defined joint in the first frame of the tracking

data, for each camera. The selected points are then triangulated, using the calibration

information, to obtain the 3D positions. From this, the length of each segment can

be calculated. If the user selects the appropriate option the length of each segment is

derived by calculating the mean value of the left and right corresponding segments.

For instance, the front coxa length (FC) is calculated by using the front left (FLC) and

right (FRC) coxa length:

FC= (FLC+FRC)/2 (3.2)

The symmetrical values are used because I assumed both sideshave the same seg-

ment lengths and this simplifies subsequent analysis of the results. Alternatively the

user can allow the model to remain asymmetric,i.e., using the raw values for the FLC

and FRC respectively. The initial values of the angles of the kinematic model are de-

rived by taking the mean value of maximum and minimum permitted value for each

angle (see table 3.1) which have been set to very generous values at the limits of plau-

sible motion. For instance the average value of the front right FTi joint is derived by

the type:

FRFTi= (MaxFTi+MinFTi)/2 (3.3)

whereMaxFTi= 180◦ andMinFTi = 0◦. An initialised model for the cricket is

illustrated in figure 3.2b.

3.3. Software 41

3.3.3 Tracker

The tracker uses constrained nonlinear optimisation to fit the kinematic model to the

joint markers extracted from each image pair, where finding and identifying each joint

marker is based on an image matching process that uses the joint positions estimated in

the previous frame. The software interface is shown in figure3.3. For convenience of

explanation I will first describe the fitting procedure, assuming that a potential match

for each joint has been extracted from the current images andtriangulated to obtain an

x,y,z position estimate. The aim is to find the optimal estimate of the model angles to

minimise the deviation of the model joint positions from theimage estimates, given

the fixed distances between joints and limits of joint motionthat has been set in the

initialisation procedure described above.

I used an active set algorithm (ASA) for constrained nonlinear optimization as

described in Hager and Zhang (2006) and implemented in the ALGLIB version 3.2

(http://www.alglib.net) library to minimize the euclidean distance between the joints

of the kinematic model and the estimated positions from the images (Equation 3.4).

Active set algorithms are a group of methods used to solve optimization problems with

equality/inequality constraints. The name of the method isderived from the fact that

at a current point each constraint is either active or inactive. The algorithm reduces

the problem from an equality/inequality constrained problem to a sequence of equality

only subproblems that can then be solved and used as the basisof an iterative process.

During this procedure, the active constraints are treated as equality ones and the in-

active are ignored. The algorithm utilizes a conjugate gradient method (Hestenes and

Stiefel, 1952) internally for the optimization.

f (x) =n

∑n=1

wn

3

∑i=1

‖pi −qi‖2 (3.4)

wherepi is the position in the model,qi the estimate, andwn is the weight for each joint.

The weights reflect the output of the matching algorithm, described further below, such

that estimates based on good matches have a higher influence on the fitting procedure,

and poor matches will have less effect.

Because the algorithm I use is not gradient-free I used a four point centre formula

to estimate the gradient for every variable.

∇ f (c) = (∂ f∂q1

(c), . . . ,∂ f∂qn

(c)) (3.5)


∂ f∂qi

(c) = 112h( f (q1, . . . ,qi −2h, . . . ,qn)

−8 f (q1, . . . ,qi −h, . . . ,qn)

+8 f (q1, . . . ,qi +h, . . . ,qn)

− f (q1, . . . ,qi +2h, . . . ,qn))

(3.6)

wherec represents the kinematic model chain andq0,q1, . . . ,qn the parameters of

the model andh = 0.1. Computation of the gradient value for every parameter takes

place in a separate thread to increase speed.

For the first image pair, the estimated joint positions are simply those chosen by

the user during the initialisation process (and all the weights are set equal to one). The

initialised kinematic model (figure 3.2b) is optimally fitted to these estimates. The 3D

model co-ordinates are then used to define the template imageof 8 X 8 pixels and

the search region for each joint marker in the next frame pair, by reprojecting their

locations to each image, and defining a window of 20 x 20 pixelsaround them. A

Kalman filter (Kalman, 1960) is also initialised and is used to predict the position of

each marker in the next frame, assuming it maintains a constant velocity, in case a

particular joint marker cannot be located in a particular image, which is usually the

result of occlusion.

The marker is located within the window using either template matching by nor-

malized cross correlation, or colour histograms (figure 3.4). I used the standard algo-

rithms available in the OpenCV library (MatchTemplate with methods

CV TM CCORRNORMED and CalcBackProjectPatch; see http://opencv.itseez.com/).

This provides both a location estimate and a value for the goodness of the match (val-

ues range between zero and one with the latter providing a perfect match). If this value

is equal or greater than a threshold value (in my case 0.8), I have a positive match. The

weight to be used in optimisation is then derived by dividingthis marker’s match value

by the minimum template matching value of the current frame:

Wi =Vi/Vmin (3.7)

Additionally, the software can utilise image filters such asbackground subtraction,

contrast and brightness adjustment, colour filtering, binary thresholding, and smooth-

ing to enhance the matching procedure. The user can apply anyof these filters by

selecting them in a property grid. During tracking image filters are applied only within

the search regions to reduce computational cost.

If a point is not positively located by the matching procedure, then the Kalman

3.3. Software 43

prediction is used to indicate the missed point if it falls within the search region. The

weight value for the predicted point is equal to the value returned by the matching

procedure (between 0.4 and 0.8). If the match value is below half of the matching

threshold value (i.e.,in my case, 0.4), or the prediction of the Kalman filter fallsoutside

the search region, the algorithm stops and the user needs to select the missed point

from the current image. Typically the algorithm works uninterrupted (i.e. tracks all

27 points without violating the stopping criteria) for about 30-80 frames. The most

common problem that stops the algorithm is the failure to track the front ThC joints.

This happens because the front coxae are almost vertical relative to the body and thus

tend to occlude the ThC joints.

Once estimates for the positions for all the joints are obtained in the current image

pair, the kinematic model is fitted to the new data, as described above. Note that the

starting point for the optimisation in this case will be the model parameters fitted to

the previous frame. At high frame rates there should be minimal change in the angles,

making this fitting procedure efficient.

The final output of the tracking procedure is the set of positions and angles for

each joint for each frame in CSV (Comma Separated Values) file format. The soft-

ware allows the user to choose to apply three different techniques for smoothing of

the angles (cubic, hermite and penalized spline interpolation) as implemented in the

ALGLIB library. It can also smooth the root joint location (in this case the metathorax

joint) by using a moving average window formula used to reconstruct the translation

(see subsection 3.3.7).

3.3.4 Grid

The Grid module is used to track the grid points on the ground,which provides a ref-

erence frame to locate the kinematic model in real space (seesection 3.3.7). The user

gives the initial global position of each point visible in the image (X,Y,Z coordinates,

with the Z coordinate set to zero,i.e. the floor) and the range of frames that each

point is tracked. The global positions can be extrapolated from the final frames of the

sequence which always include an identified grid point on theedge of the arena (see

section 3.2.1). The tracking algorithm used is the same as the tracker module, except

in this case there is only one point tracked in each of the leftand right frames. The grid

coordinate positions are also smoothed by using a moving average window formula.


(a)

TiTa

TiTa

FTi

TiTa

FTi

CTr

CTr

FTi

ThC

CTr

ThC

ThC

ThC

ThC

ThC

CTr

FTi

CTr

CTr

TiTa

FTi

FTi

TiTa

TiTa

(b)

β

α

γ

β

β

(c)

β

α

γ

β

β

(d)

βα

β

β

γ

α

(e)

Figure 3.2: The kinematic model of the cricket. (a) The painted joints of the cricket’s

body and legs are indicated with yellow dots and the segments with cyan lines. See

table 3.1 for corresponding joint definitions. (b) shows the model with the initial angles

based on taking the midpoint of the defined limits of the angles for each segment. Each

joint potentially has three axes of rotation: roll α around the X axis (red); pitch β around

the Y axis (green); and yaw γ around the Z axis (blue). (c), (d), (e) show the joints of

the right hind, middle and front leg of the fitted model in the tracked data for a frame.

The purple lines indicate the tracked points and the blue lines the corresponding model.

Each leg has 3 DOF at the thoraco-coxal joint, and one DoF (β) at the coxo-trochanteral

and femoro-tibial joints; the front legs have an additional constant rotation (α) at the

trochanter-femur joint (co-located with the coxo-trochanteral joint).

3.3.5 Sound

The sound module is used to determine the sound state (on or off) and direction (which

end of the arena) by monitoring the LED indicators. In order to do that the user selects

the area in the image corresponding to each LED and gives a threshold for the colour.

3.3. Software 45

Joint Parameters Limits

Metathorax (0) x, y, z [x0−0.5 x0+0.5], [y0−0.5 y0+0.5], [z0−0.5 z0+0.5]

Segment Parameters Limits

Metathorax - mesothorax (0 - 1) α, β, γ [−30◦ 30◦], [−30◦ 30◦], [−180◦ 180◦]

Middle right coxa (2 - 3) α, β, γ (ThC) [−90◦ 90◦], [−30◦ 90◦], [−90◦ 90◦]

Middle right femur (3 - 4) β (CTr) [−180◦ 0◦]

Middle right tibia (4 - 5) β (FTi) [0◦ 180◦]

Middle left coxa (6 - 7) α, β, γ (ThC) [−90◦ 90◦], [−30◦ 90◦], [−90◦ 90◦]

Middle left femur (7 - 8) β (CTr) [−180◦ 0◦]

Middle left tibia (8 - 9) β (FTi) [0◦ 180◦]

Mesothorax - prothorax (1 - 10) α, β, γ [−30◦ 30◦], [−30◦ 30◦], [−30◦ 30◦]

Front right coxa (11 - 12) α, β, γ (ThC) [−90◦ 90◦], [−30◦ 160◦], [−90◦ 90◦]

Front right femur (12 - 13) α (TrF), β (CTr) 45◦, [−180◦ 0◦]

Front right tibia (13 - 14) β (FTi) [0◦ 180◦]

Front left coxa (15 - 16) α, β, γ (ThC) [−90◦ 90◦], [−30◦ 160◦], [−90◦ 90◦]

Front left femur (16 - 17) α (TrF), β (CTr) −45◦, [−180◦ 0◦]

Front left tibia (17 - 18) β (FTi) [0◦ 180◦]

Hind right coxa (19 - 20) α, β, γ (ThC) [−90◦ 90◦], [−90◦ 90◦], [−120◦ 0◦]

Hind right femur (20 - 21) β (CTr) [−180◦ 0◦]

Hind right tibia (21 - 22) β (FTi) [0◦ 180◦]

Hind left coxa (23 - 24) α, β, γ (ThC) [−90◦ 90◦], [−90◦ 90◦], [0◦ 120◦]

Hind left femur (24 - 25) β (CTr) [−180◦ 0◦]

Hind left tibia (25 - 26) β (FTi) [0◦ 180◦]

Table 3.1: The joints and segments of the model with parameters of the model and their

limits. The units for the joint limits are in millimetres and for the angles in degrees. I

used very generous limits due to the lack of prior information regarding the motion of

the cricket. For instance all the FTi joints can rotate from −180◦ to −0◦ which leads to

full flexion and extension of the tibia, which will never happen during the normal insect

walking patterns.

When the LED is on this means that the colour is brighter and therefore the algorithm

determines the sound as on. There is no significant delay between the sound output and

the LED flashing as the circuit controls both outputs at the same time. Note, however,

that as sound syllables are only 21 ms in duration, I may miss exact onset or offset by up

to 3.33 ms due to frame rate. I can solve this problem by resampling the sound output

as if it was every 1 ms and fitting the known pattern of the song by cross-correlating

the two patterns. Note that the LEDs are attached to the triangular frame holding the

cameras, and thus always stay in the same position in the image as the cameras are

moved to follow the animal. Thus no additional tracking is required. However, the

user needs to keep the camera frame positioned correctly around the animal to avoid

overlapping of cricket appendages by the LEDs.


Figure 3.3: A screenshot of the tracker module of the software. The two images show

the corresponding left and right frames containing the insect, the grid points and the

LEDs in the bottom. The user can save the data, fit the kinematic model in the selected

frame, begin tracking, save a video with the tracked points, reset settings and load the

appropriate help file by pressing the top buttons. The viewer tab shows the tracked

joints and segments in the OpenGL environment. The video buttons can be used to

play the video or move to a certain position. The current joint can be selected from the

combobox or by using the keyboard shortcuts. Information about the selected point co-

ordinates are also displayed. Each frame can be panned or zoomed by using the mouse

or the keyboard shortcuts. Undo-redo functions are also supported. Information about

each segment such as the length can be seen in the middle right area. The settings for

filtering the images and changing options for the algorithms and the appearance can

be set in the property grid in the bottom right corner.

3.3.6 Stance-Swing

The stance - swing module is used to output the stance - swing state of each leg. The

states for every leg can be determined by taking the Z coordinates of the TiTa joints

from the tracker data,i.e., swing is defined as whenever this joint has a Z value above

a threshold. In practice, the threshold Z-value for swing needs to be set separately for

each pair of legs (front, middle, or hind) due to the difference the leg geometries. Hind

leg TiTa joints touch the ground in stance, while front and middle leg TiTa joints do not

touch the ground. Typical values for the threshold were found experimentally (1.0 mm

3.3. Software 47

Figure 3.4: The procedure of tracking a point. The user selects the point in the frame n

(first inset box) and the algorithm searches for the point in the region of the frame n+1

(second inset box). The best match is indicated by the brightest area (third inset box).

Image filters have been removed for simplicity.

for the front and middle legs and 0.2 mm for the hind legs). Thesystem also allows

manual setting of stance state for each frame; this can be done before the tracking (i.e.,

before coordinate information is available) or to correct any inconsistencies in the data

that are observed by the user. The module visualizes the stance - swing states and the

Z coordinates for every leg to ease this procedure.

3.3.7 Player

The player module (figure 3.5) combines all the information from the other modules to

visualize the tracked cricket. First of all it calculates the difference of the tracked grid

coordinates from the user defined coordinates given in the grid module (3.3.4). This

gives the offset of the grid points and the cricket points.

To calculate the offset the following type is used:

To f f set=Creal−CgridPosition (3.8)

By using the above results I can calculate the global positionof the cricket points

by using the following type:

Cglobal = To f f set+Ctracker (3.9)

This module can replay the motion of the cricket as it walks inthe arena and ob-

serve from any angle. It can visualize the stance-swing transitions and the sound pat-

tern. Furthermore, this module outputs the smoothed data (figure 3.11a) in CSV files

for subsequent analysis. Also, resetting the translation and rotation of the metathorax

to be zero reveals the relative motion of all the joints as if the insect had been fixed in


one position. This means the free walking data can be used forcomparison with sta-

tionary setups such as a cricket on a trackball turning (figure 3.11b) or forward walking

(figure 3.11c).

Figure 3.5: A screenshot of the player module of the software. The two boxes represent

the speakers. The right speaker is red to indicate that the sound is ON at the specific

frame. The player can show various properties of the data such as the path of the insect

(2), the stability polygon (3) and the legs that are in swing state (4).

3.4 Results

To illustrate the tracker output, I present results from onerecording sequence of the

insect. In this scenario the sound is played from the right speaker while the insect starts

facing towards the left speaker. When the sequence begins theinsect first turns almost

on the spot to face the right speaker and then starts walking towards the right speaker

until the string is stretched. I processed in total 27×2×6730+2×7887= 379,194 2D

points for the tracking of the insect and grid points, for these video sequences. I used

an HP 6735s laptop (AMD AthlonTMX2 DualCore QL-60 1.9 GHz processor, 4GB

RAM). The optimization time for the first frame takes about 20-25 secs and for the

subsequent frames 3-6 secs. The following figures show the data produced: the angles

of all the body joints (figure 3.6) and for the joints of the front (figure 3.7) , middle

(figure 3.8) and hind (figure 3.9) legs; a summary of the stance-swing transitions (figure

3.4. Results 49

3.10); and the reconstructed track and the motion of the feetrelative to the body (figure

3.11). From this a number of interesting features can be observed. More detailed

analysis of these and other features will be presented in thenext chapter.

There is clear movement of the mesothorax, including rhythmic bending of the

mesothorax-prothorax joint coupled to the stepping pattern in forward walking (figure

3.6). Both metathorax and mesothorax exhibit some roll into the turn. I should also

note that there is clear movement of the abdomen, neck and antennae in all the videos

but the monitoring of these movements is beyond the scope of the current experiments.

There are obvious differences between turning and forward walking. During for-

ward walking all body and leg joints follow a regular pattern, while in the case of turn-

ing there is much more variability in the values of the angles. However, it is evident

that all DoF are used in both forward walking and turning. Thedata are broadly con-

sistent with previous analyses of a cricket performing forward walking without sound

stimulus (Laksanacharoen et al., 2000), except that a clearer contribution of the ThC

joint to the hind-leg motion, and of the CTr joint to the front leg motion is observed.

In the approach to sound, there are at most two legs at a time inswing phase and

the insect never achieves a tripod gait (figure 3.10). There is no apparent coupling

between the chirp pattern and the onset of the stepping cycles (figure 3.10). From the

stance patterns, and in figure 3.8, it is obvious that the ipsilateral middle leg is not

stepping much during the turn, whereas the contralateral front and middle legs take

a higher number of steps, and both hind legs take fewer steps.This is in contrast to

experiments on a trackball (Witney and Hedwig, 2011). The ipsilateral hind leg also

makes smaller movements during turning. In figure 3.11 it is apparent that the front

and middle legs ipsilateral to the sound pull the body towards the speaker direction,

while the contralateral front and middle legs push the body.Similar contribution of

the middle legs has been observed in cockroaches (Mu and Ritzmann, 2005) and stick

insects (Gruhn et al., 2009).


Figure 3.6: Results for the body joint angles: red line is raw data and blue is smoothed

data. The green line indicates the end of a right turn by the insect and the beginning of

the forward walking towards the sound source. Each frame corresponds to 3.33 ms.

3.4. Results 51

Figure 3.7: Results for the front legs’ joint angles: red line is raw data and blue is

smoothed data. The green line indicates the end of a right turn by the insect and

the beginning of the forward walking towards the sound source. The grey rectangles

represent the swing phases for each leg. Each frame corresponds to 3.33 ms.


Figure 3.8: Results for the middle legs’ joint angles: red line is raw data and blue

is smoothed data. The green line indicates the end of a right turn by the insect and

the beginning of the forward walking towards the sound source.The grey rectangles


3.4. Results 53

Figure 3.9: Results for the hind legs’ joint angles: red line is raw data and blue is

smoothed data. The green line indicates the end of a right turn by the insect and

the beginning of the forward walking towards the sound source. The grey rectangles



(a)

(b)

Figure 3.10: The stance swing transitions for all the legs for (a) right turn and (b) forward

walking for the first and last 1800 frames (12 secs) for the same walking sequence as in

figures 3.6-3.9. Stance with sound off is marked with black and with sound on with red

colour. Swing with sound off is marked with white and swing with sound on is marked

with purple colour. From top to bottom, front right (FR), middle right (MR), hind right

(HR), front left (FL), middle left (ML) and hind left (HL). Notice in the turn the higher

number of swing transitions of the left side legs compared to the right legs and the

speed of the legs during swing phase in general. Each frame corresponds to 3.33 ms.

3.4. Results 55

(a)

(b) (c)

Figure 3.11: The transformed smoothed (a) path from the combination of tracking infor-

mation and the grid points, using the TiTa joints and the thorax joints. The cyan colour

indicates the stance phase of the legs, the purple colour the swing phase and the blue

colour the thorax position. I can also reconstruct the position of the insect as it would

be fixed in one position by simply resetting the translation and rotation of the metatho-

rax. These are the smoothed results taken from the walking sequence shown in (a) and

separated into the turning (b) and walking (c) component of the sequence. This allows

the comparison of the performance with trackball results such as those by Witney and

Hedwig (2011).


Finally, I compare the joint position and angle estimates resulting from the tracker

to the same points estimated by manual digitisation. I digitised every tenth frame

throughout the sequence, providing a total of 500 estimatesof the tracking ‘error’

for each joint. As shown in figure 3.12(a), the difference wasin general less than

0.5mm, with the largest differences occurring for the hind CTr joint still less than 1mm.

This compares favourably to the results in Bender et al. (2010) which fall between

0.5mm and 1.5mm. However, note that this could as well be interpreted as the size

of ‘human error’ relative to the automated tracking and model fitting as vice-versa; in

particular, fitting the model is essentially a method that corrects for any errors in precise

localisation of the marker. I then used the law of cosines to estimate the maximum

difference in estimates of angles that could be produced by the observed difference in

the position estimates (figure 3.12(b)).

arccos2×segmentLength2− jointDi f f erence

2×segmentLength2(3.10)

Note that for each joint this is done for one degree of freedom. Therefore for 3

DoF joints (ThC) I am estimating based on a ‘worst case’ scenario in which the error

is assumed to be entirely due to one of these DoF while the other two have zero error.

Not surprisingly, given that the coxa segments are quite small (for example hind coxa

is 1.6 mm), this methods results in a relatively large maximum error estimate for the

ThC joint angles.

3.4. Results 57

(a)

(b)

Figure 3.12: (a) Deviation in tracking acuity (in mm) by the tracking algorithm compared

to manual digitization for 500 frames. The difference is generally less than 0.5 mm.

(b) Estimated maximum error in joint angles (in degrees) that could result from the

observed deviation in tracking acuity.

Chapter 4

Analysis

4.1 Introduction

In the previous chapter I described a method to track the joints of a female cricket while

it performed phonotaxis. In this chapter I present an analysis based on the results

from all the videos I recorded when the crickets responded tothe sound signal. All

specimens were given up to 5 minutes to respond. These results are based on 16

different crickets. There are 2 experiments per cricket andtherefore 32 experiments in

total. Sound was presented 19 times from the right speaker and 13 from the left speaker.

There are 17 right turns and 15 left turns. There are four combinations of speaker -

turn direction (11 times right speaker - right turn, 8 times right speaker - left turn, 6

times left speaker - right turn and 7 times left speaker - leftturn). The first 8 crickets

were tracked for both turning and forward walking, while thelast 8 crickets were

tracked only for the initial turning, to gather more information and samples regarding

the turning behaviour. In total, 308,398 grid points and 4,902,768 tracker points were

obtained, giving a total of 5,211,166 2D points, resulting in 2,605,583 3D points, most

of which were tracked by the tracking software.

Table 4.1 presents a summary of the overall video processing. I define turn latency

as the period from the first sound pulse played by the speaker until the first anten-

nal movement. Only one cricket responded to the first chirp. Average turn latency

was 52.54±55.55 sec (mean±SD) with maximum latency 208.13 sec, minimum la-

tency 0.27 sec, median latency 34.84 sec and kurtosis 3.14 (for distributions see figure

4.1). Average turn duration was 3590.63±1636.09 ms with maximum duration 8570

ms, minimum duration 1636.09 ms, median duration 3815 ms andkurtosis 3.85 (for

distributions see figure 4.1). These values take into account periods when some of

59

60 Chapter 4. Analysis

the crickets had stopped during the turn. Most crickets did not stop after the turn

(N=27) but about half of them stopped at least one time duringturning or forward

walking (N=14). This is in contrast with earlier studies where there was no disconti-

nuity in the movement between the turn and subsequent forward walking (Scapsipedus

marginatus; Murphey and Zaretsky, 1972). I defined a stop as a period of atleast 90

frames (≈300 ms), a value within the stepping cycle reported in earlier studies (Lau-

rent and Richard, 1986b; Witney and Hedwig, 2011), where all the legs were touching

the ground and the rest of the body was not moving. These results include the crickets

that were tracked only during turning but the videos also include their forward walking

behaviour and therefore I was able to determine the number oftimes they had stopped.

Although not explicitly tracked, it was observed in the videos that the insects’

antennae are the first moving part and scan the area in front ofthe head. The time

taken from the first move of the antennae until the cricket lifted its first leg in order

to turn was between 10 and 1806 ms with a mean value 443.54±416.9 ms, median

value 346.67 ms and kurtosis 5.8 (for distributions see figure 4.1). Both antennae

turn towards the direction of turning when the cricket initiates its turn. Also, head

orientation is shifted towards the turning direction. Thislead response of the antennae

and the head has been observed before in stick insects (Durr and Ebeling, 2005).

In the following sections, I address specific questions thatwere posed in chapter

2. Note that a direct comparison with results from other studies on crickets or other

insects is not possible, since the methodologies followed either use more constraints

on the insect motion and therefore movements are not exactlysimilar to freely walking

conditions, or if the insect is walking freely the study doesnot provide the amount of

information gained with the method followed in this thesis.First I analyse the results

of the individual leg and body angles (section 4.2). Then I present the leg coordination

results (section 4.3). Furthermore, I analyse the angles ofthe thorax and the ears in the

front legs with respect to the centre of the speakers (section 4.4). Finally, I present an

estimation of the ears input while the cricket performs its phonotactic response (section

4.5).

4.1. Introduction 61

C E S TD TL TF FT TT TS SN AM

01 01 R R 13890 7944 6731 1801 0 0 106

01 02 L R 8003 6593 5251 431 0 0 103

02 01 L L 28927 6593 5641 1474 281 1 72

02 02 R L 31059 10040 9311 1051 0 2 338

03 01 R R 12898 5395 4901 621 0 1 53

03 02 L R 6681 4796 4311 583 0 1 10

04 01 R R 2928 3147 2561 480 0 0 3

04 02 L L 2447 4945 4301 852 941 1 105

05 01 R R 21692 5994 4091 1471 0 0 161

05 02 L L 19294 8092 7171 2571 0 1 542

06 01 R R 4116 5844 4201 1351 0 0 159

06 02 L L 4553 4496 2861 583 0 1 96

07 01 R L 1561 3746 3161 561 0 0 60

07 02 L R 384 3896 3091 1281 0 1 123

08 01 R R 311 3896 2751 1186 0 0 458

08 02 L R 489 3297 2271 582 0 1 17

09 01 L R 22262 2247 971 884 0 0 33

09 02 R L 39280 3296 1701 1680 0 0 69

10 01 L L 13012 3597 1841 1841 0 0 192

10 02 R R 26658 2397 911 911 0 0 61

11 01 R R 82 1799 861 861 0 0 106

11 02 R L 179 2247 1261 1232 0 0 234

12 01 R L 4649 9140 541 436 0 2 49

12 02 R L 31329 5850 1281 1148 881 2 84

13 01 L L 41469 2098 1191 1191 0 0 216

13 02 R L 16684 1348 671 620 0 0 41

14 01 R R 479 2847 1401 1374 0 0 12

14 02 R R 39628 4495 1391 1322 0 0 157

15 01 R L 44724 10039 1231 1231 0 2 231

15 02 L L 62439 9740 1211 1211 1235 2 205

16 01 L R 574 1648 581 508 0 0 11

16 02 R R 1718 2697 1141 1141 271 1 151

Table 4.1: Time properties for all the experiments. Cricket number (C), experiment

number (E), speaker(S), left (L), right (R), turn direction(TD), turn latency(TL), total

frames (TF), tracked frames (FT), turn time frames (TT), stop duration after turn in

frames (TS), number of stops (SN) and antennae and head movement frames before

first leg swing (AM). Each frame corresponds to 3.33 ms.


Figure 4.1: Distributions of time properties for all the experiments. Turn latency (top

left), turn duration (top right) and antennae movement (bottom).

4.2 Single Leg and Body Angles

In this section I address the following questions:

• What is the motion of each thoracic segment and body part during forward walk-

ing and turning?

• What is the motion of each individual leg joint during forwardwalking and turn-

ing?

• What are the similarities and differences with other insects?

I distinguish between the initial left and right turn when the crickets turn to orient

themselves towards the sound source and the rest of the walking behaviour which is

termed as forward walking. Note that the walking patterns produced are never perfectly

straight and each side of the insect does not produce exactlythe same body and leg joint

4.2. Single Leg and Body Angles 63

patterns. I define the beginning of turning as the time when the insect begins moving its

antennae and head. In order to determine the end of turning, Iinspected the videos and

the 3D reconstruction of the insect in the Player module, to find when the insect began

walking approximately forward after the initial turn. All the legs had completed their

step cycle before the defined end of the turn. In most turns thecrickets either turn on

the spot or produce a U-shaped turn pattern similar to previous results on honeybees’

turning behaviour (Zolotov et al., 1975). However, in contrast to the aforementioned

study and results from tethered stick insects (Gruhn et al.,2009) and freely walking

flies (Strauss and Heisenberg, 1990), it was observed that the ipsilateral middle leg

halted during turning rather than the ipsilateral hind leg.

I first gathered the periods where each leg was in swing mode. Then, based on

the coordinates of the TiTa joints at the beginning and at theend of swing I calculated

the average distance that each leg had travelled. The same procedure was also per-

formed for the trackball coordinates (i.e. distances relative to the cricket’s own body).

Furthermore, I calculated the number of swing transitions that each leg had made.

It is clear that during right turns the left legs from all the thoracic segments cover

longer distance than those on the right side (table 4.2) and correspondingly the oppo-

site case happens during left turns. This is in contrast withresults from stick insects

where the front contralateral to the turn leg had the same stride length as forward walk-

ing (Gruhn et al., 2009). Change in the step length on one side relative to the other has

been observed before in experiments in stick insects (Graham, 1972) and cockroaches

(Franklin et al., 1981) and on a trackball in crickets (Witney and Hedwig, 2011). Dur-

ing forward walking both sides cover the same distances. Thefront and the middle

legs cover similar distances, while the hind legs cover slightly longer distances. This is

easily explained by the length of the leg segments. However,the trajectory of the front

and middle legs is more like an arc and the hind legs’ is closerto a straight line (figure

4.2).

If the same properties are calculated for the converted trackball coordinates, they

produce similar results (table 4.3). The only difference isthat the legs cover smaller

distances. This is expected because if the insect is fixed on atrackball, its body is not

moving forward as the legs move.

The number of swings and the timing of the leg movements also reveals the con-

tribution of each leg to turning (table 4.4). The most clear difference with respect to

turning regards the middle legs. The middle leg ipsilateralto the turn is lifted off the

ground only a quarter to a half as many times as the contralateral middle leg, as the


L RT MV RT SD LT MV LT SD FW MV FW SD

FR 4.74 ±1.46 6.83 ±2.05 8.47 ±2.68

FL 7.21 ±2.47 4.76 ±1.82 8.50 ±2.98

MR 5.26 ±1.86 6.69 ±2.22 8.38 ±3.11

ML 7.49 ±2.13 4.65 ±1.98 8.26 ±3.07

HR 5.13 ±1.99 8.46 ±2.00 9.38 ±2.88

HL 9.78 ±2.57 5.37 ±2.22 9.64 ±2.79

Table 4.2: Mean values (MV) and standard deviations (SD) of swing distances for the

front right (FR), front left (FL), middle right (MR), middle left (ML), hind right (HR) and

hind left (HL) legs (L) in millimetres, during right turn (RT), left turn (LT) and forward

walking (FW). Note some variation is due to different size animals.

L RT MV RT SD LT MV LT SD FW MV FW SD

FR 3.74 ±1.25 5.63 ±1.62 5.81 ±1.61

FL 6.01 ±2.11 3.78 ±1.51 5.90 ±1.87

MR 4.45 ±1.51 5.30 ±1.80 5.73 ±2.02

ML 5.99 ±1.71 3.96 ±1.70 5.62 ±2.04

HR 4.32 ±1.69 7.10 ±1.83 6.87 ±2.11

HL 8.23 ±2.05 4.33 ±1.80 7.22 ±1.92

Table 4.3: Mean values (MV) and standard deviations (SD) of trackball swing distances

for the front right (FR), front left (FL), middle right (MR), middle left (ML), hind right (HR)

and hind left (HL) legs (L) in millimetres, during right turn (RT), left turn (LT) and forward

walking (S). Note some variation is due to different size animals.

insect turns to orient itself towards the speaker. This is incontrast with experiments

on a trackball (Witney and Hedwig, 2011) where no change in stepping frequency was

found. The front and the hind legs produce almost the same number of swings during

turning and forward walking, although with slightly less number of swings on the ip-

silateral to the turn side. Also the hind legs produce fewer swings compared to front

legs during turning.

In terms of timing, the front and the middle legs take a similar amount of time for

their swing during turning and the hind legs take more time. During forward walking

the front and the hind legs take the same amount of time to perform their swing while

the middle legs take less time. The ratio of protraction/retraction is significant lower for

the ipsilateral to the turn middle leg as expected (table 4.5). During forward walking


L RT N RT MV RT SD LT N LT MV LT SD FW N FW MV FW SD

FR 80 80.38 ±25.91 79 84.68 ±28.20 433 82.22 ±19.65

FL 87 77.97 ±26.09 73 92.92 ±33.60 450 82.26 ±19.52

MR 23 78.41 ±28.94 80 78.67 ±25.65 426 67.21 ±19.70

ML 76 78.33 ±28.71 39 74.19 ±21.04 432 65.88 ±18.06

HR 54 99.63 ±40.74 64 114.01 ±35.94 432 81.64 ±19.11

HL 63 114.07 ±40.41 55 114.42 ±50.17 427 81.52 ±20.66

Table 4.4: Number of swings (N), mean values (MV) and standard deviations (SD) of

swing duration for the front right (FR), front left (FL), middle right (MR), middle left (ML),

hind right (HR) and hind left (HL) legs in milliseconds, during right turn (RT), left turn

(LT) and forward walking (FW).

the ratio is similar for the front and hind legs. This in contrast with previous results in

crickets (Acheta domesticus; Harris and Ghiradella, 1980) and cockroaches (Delcomyn

and Usherwood, 1973) where the hind legs spend more time during forward walking

in swing than the front and middle legs. This might be explained by the fact that even

if the hind legs are longer, the front legs have larger range of movements. Also since

the percentage of each leg on the ground is very different forthe middle legs this is

in contrast with results in cockroaches (Franklin et al., 1981) where all six legs had

similar protraction/retraction rates during turning.

L RT MV RT STD LT MN LT STD FW MN FW STD

FR 0.22 ±0.17 0.19 ±0.10 0.32 ±0.17

FL 0.25 ±0.33 0.24 ±0.38 0.33 ±0.13

MR 0.07 ±0.04 0.25 ±0.49 0.25 ±0.16

ML 0.26 ±0.36 0.09 ±0.06 0.25 ±0.11

HR 0.20 ±0.29 0.26 ±0.25 0.31 ±0.10

HL 0.23 ±0.15 0.18 ±0.13 0.30 ±0.11

Table 4.5: Mean values (MV) and standard deviations (SD) of ratio of protraction /

retraction during right (RT) and left (LT) turns and forward walking (FW).

In order to calculate the change in angle of each joint duringeach stride I first

grouped all the samples of angles that are from the beginningof stance to the end of

swing for the left and right turns and forward walking. I did not take into account

the periods when the insects were stopped. Then, I used cubicspline interpolation


provided by the MATLAB’s spline function to resample the angle values into 100

values for each sample. Then I calculated the mean and standard deviation of these

samples. There are 180 figures in total (5 joints per leg× 6 legs× 3 types of walking

+ 5 body angles× 6 legs× 3 types of walking), most of which are provided in pages

70–77. I present three columns with figures for the forward walking in the left, right

turn in the middle and left turn in the right. Because each insect only made a few

steps during its initial turn, there are obviously fewer samples for the turns than for

the forward walking. This is especially salient for the inside middle legs during the

corresponding turn, since the leg is lifted off the ground very few times.

Many of the joint movements of the cricket are different between forward walk-

ing and turning. During forward walking the legs of each thoracic pair make similar

movements, while during turning there are obvious differences between all three pairs

of legs. This in contrast with experiments on a trackball (Witney and Hedwig, 2011)

and an arena (Scapsipedus marginatus; Murphey and Zaretsky, 1972) in crickets and

stick insects (Durr and Ebeling, 2005) where only the front and middle legs presented

changes. This can possibly be explained by the fact that the hind legs are the ones

affected most by the movement of the trackball. Also, the lowtemporal resolution of

the video recordings in earlier studies in an arena may have limited the accuracy of the

leg trajectory tracking.

The front leg ipsilateral to the turn direction increases its lateral movement while

it decreases its forward movement (figure 4.2). The front legcontralateral to the turn

direction increases its forward and lateral movement. The middle leg ipsilateral to the

turn is rarely lifted off the ground during turning and presents the smallest movement

of all the legs; decreasing its forward movement and increasing its lateral movement.

In contrast, the middle leg contralateral to the turn increases its forward and lateral

movement. The hind leg ipsilateral to the turn decreases itsforward movement and

increases its lateral movement. The hind leg contralateralto the turn increases its

forward and lateral movement. The prothorax moves towards the direction of the turn.

It is worth noting that during turns the ipsilateral to the turn middle leg’s TiTa joint is

the centre of a circle formed by the stance points. This fact suggests that a two-wheeled

robot that limits the movement or even stops one of its wheelsis actually a reasonable

approximation to the crickets turning behaviour.

The results of the individual joints show obvious differences during forward walk-

ing, left turn and right turn. I will refer to the differencesin the pattern in the right side

for every thoracic segment. This means that for the right turn this will be the inside leg


Figure 4.2: Top view of leg patterns during forward walking (top), right turn (bottom left)

and left turn (bottom right). Thorax is depicted with green colour, legs with blue and

trajectories with black. Note that the stance segments during turns appear to fall on a

circle with its centre to the ipsilateral to the turn middle leg’s TiTa joint.

and for the left turn the outside leg. The differences in the left side are similar, given

that some of the joints are expected to have the exact opposite pattern. For instance the


ThCα have opposite patterns in all the leg pairs. The results for the middle legs when

the leg is ipsilateral to the turn side are the noisiest ones since there are less samples

for the middle legs.

I will now briefly mention the effect of each joint DoF to the leg and body move-

ment, before presenting the results. ThCα rotates the coxa around its long axis. ThCβ

moves the coxa up or down. ThCγ moves the coxa forwards or backwards. CTrβ moves

the femur up or down. FTiβ moves the tibia in or out. Metathorax - mesothoraxα rotates

the metathorax - mesothorax segment around its long axis. Metathorax - mesothoraxβ

moves the metathorax - mesothorax segment up or down. Mesothorax - prothoraxα ro-

tates the mesothorax - prothorax segment around its long axis. Mesothorax - prothoraxβ

moves the mesothorax - prothorax segment up or down. Finally, the mesothorax -

prothoraxγ moves the mesothorax - prothorax segment right or left. See the first col-

umn of the figures in pages 70–77 for visualization.

The front right leg (figure 4.3), during forward walking stance phase increases

the ThCα, ThCβ and FTiβ angles and decreases the ThCγ and CTrβ angles. Then

during swing it decreases the ThCα, ThCβ and FTiβ angles and increases the ThCγ and

CTrβ angles. During right turn the ThCα angle remains the same throughout the step.

The ThCβ increases during stance and decreases during swing as before. The ThCγ

remains the same during stance until the last part when it increases and decreases

during swing. The CTrβ and FTiβ have similar pattern with forward walking. During

left turn the ThCα and ThCγ have similar pattern with forward walking. The ThCβ

is decreased during stance and increased during swing. The CTrβ remains the same

during stance and first decreases and then increases during swing. The FTiβ remains

constant throughout the step. This observation for the front leg contralateral to the turn

has been made before, although in less detail (Witney and Hedwig, 2011).

The middle right leg (figure 4.5), forward walking stance phase increases ThCα,

ThCβ and CTrβ angles while the ThCγ angle decreases and the FTiβ angle initialy

remains stable and then decreases. During swing the ThCα, ThCβ and CTrβ angles

decrease while the ThCγ and FTiβ angles increase. During right turn the ThCα and

ThCβ remain constant, while the ThCγ increases during stance and decreases during

swing. The obvious differences with forward walking is thatthe CTrβ decreases during

stance and increases during swing while the FTiβ follows the opposite pattern. This

has been observed before in cockroach escape response (Nye and Ritzmann, 1992).

During left turn all the joints have similar values with the forward walking. The FTiβ

has the opposite pattern than right turn.


The hind right leg (figure 4.7), forward walking stance phaseincreases ThCα and

CTrβ and decreases ThCγ and FTiβ. ThCβ remains constant throughout the step cycle.

During swing ThCα and CTrβ decrease and ThCγ and FTiβ increase. During right

turn ThCα remains constant while ThCγ increases during stance and decreases during

swing. ThCβ decreases during stance and increases during swing. CTrβ and FTiβ

angles have similar patterns with forward walking, but smaller range of values. During

left turn ThCα and ThCγ have similar values to forward walking. ThCβ increases

during stance and decreases during swing. CTrβ and FTiβ values are similar to the

right turn values.

In figures 4.9 - 4.10 I present the effect at the body angles relative to the step se-

quence of the front legs. The effect relative to middle and hind legs is similar. Metatho-

rax - mesothoraxα remains the same for forward walking and turning. Metathorax -

mesothoraxβ and Mesothorax - prothoraxβ slightly decrease during turning. Mesotho-

rax - prothoraxα and Mesothorax - prothoraxγ have similar patterns during forward

walking and turning but with different values, reflecting the bending of the body in

the direction of the turn. Therefore the bending of the prothorax contributes to the

positioning of the front legs as has been observed before on atrackball (Witney and

Hedwig, 2011).


Figure 4.3: Mean, standard deviation of angles values and number of samples (N) for

the front right leg during forward walking (left), right (middle) and left (right) turn. 0%

represents the start of stance and 100% the end of swing.



the front left leg during forward walking (left), right (middle) and left (right) turn. 0%




the middle right leg during forward walking (left), right (middle) and left (right) turn. 0%




the middle left leg during forward walking (left), right (middle) and left (right) turn. 0%




the hind right leg during forward walking (left), right (middle) and left (right) turn. 0%




the hind left leg during forward walking (left), right (middle) and left (right) turn. 0%



Figure 4.9: Mean, standard deviation of angles values for the thorax and number of

samples (N) relative to the stance-swing cycle of the front right leg during forward walk-

ing (left), right (middle) and left (right) turn. 0% represents the start of stance and 100%

the end of swing.


Figure 4.10: Mean, standard deviation of angles values for the thorax and number of

samples (N) relative to the stance-swing cycle of the front left leg during forward walking

(left), right (middle) and left (right) turn. 0% represents the start of stance and 100%

the end of swing.


In order to transition from forward walking to turning thereare some joints in each

leg that play a crucial role. For the front legs during contralateral turn the freezing of

the CTr and FTi joints are the most important, while for the ipsilateral turn reducing the

movement of all ThC joints produces inside leg motion (figure4.11). For the middle

legs reversing the movement of FTi and CTr joints and reducingthe movement of ThC

joints would produce ipsilateral turning, while for the contralateral turning the ThC

joints angles would slightly change (figure 4.12). The ThCγ would need to reverse

the movement to produce inside turning and have larger rangeof motion to produce

contralateral turning (figure 4.13).

Note here that the contribution of all three DoF in the ThC joint to the leg motion

make the results difficult to compare to standard models thatreduce this joint move-

ment to one DoF. Note also that crickets are making turns - or rather applying small

corrections to their heading direction - during forward walking. This is likely to in-

volve much more subtle changes to joint angles than sharp turns. Another general

observation is that the patterns of the angles are not sinusoidal and the change in direc-

tion is not necessarily aligned with stance - swing transition. I found no evidence that

the walking pattern or any leg angle variation is correlatedto sound pattern (results not

shown). This was also the result in previous studies using a trackball (Baden and Hed-

wig, 2008; Witney and Hedwig, 2011). Finally, because of themethodology I used, it

is difficult to separate the contribution of muscle activityand the mechanical structure

to the pattern of change. Slippery surface setups that decouple the legs between them

could provide supplementary data to this method (Gruhn et al., 2006; Bender et al.,

2010).


Figure 4.11: Front right leg joints’ inside and outside turns contributions. Freezing the

motion of all ThC joints at 80% of the step cycle produces similar pattern to ipsilateral

turning (top). Freezing the movement of CTr and FTi joints at 80% of the step cycle

(appoximately at the beginning of swing) in the forward walking joints values produces

similar step pattern to the contralateral turning (bottom). Thorax is depicted with green,

leg with blue, forward walking pattern with black, turn pattern with purple and pattern

with modified joint angles values with cyan colour.


Figure 4.12: Middle right leg joints’ inside and outside turns contributions. Freezing the

motion of all ThC joints at 80% of the step cycle and reversing the motion of CTr and

FTi joints produces similar pattern to ipsilateral turning (top). Freezing the movement

of CTr and FTi joints at 80% of the step cycle (appoximately at the beginning of swing)

in the forward walking joints values produces similar step pattern to the contralateral

turning (bottom). Thorax is depicted with green, leg with blue, forward walking pattern

with black, turn pattern with purple and pattern with modified joint angles values with

cyan colour.


Figure 4.13: Hind right leg joints’ inside and outside turns contributions. Altering the

movement of the Thγ joint generates smaller movement for the ipsilateral turn (top) and

larger movement for the contralateral turn (Bottom). Thorax is depicted with green, leg

with blue, forward walking pattern with black, turn pattern with purple and pattern with

modified joint angles values with cyan colour.


4.3 Leg Coordination


• What is the coordination of leg swing during forward walking and both right and

left turns?

• Can the existing coordination rules (Cruse et al., 1991) explain the leg coordina-

tion results from the experiments?

During right turning the front left (6 times), middle left (5times) the hind left (3

times) and front right (3 times), are the first legs to lift offthe ground. During left

turning the front right leg (5 times), the middle right leg (4times), the hind right leg (3

times) and the front left (3 times). This is different from results in fliesOrmia ochracea

(Mason et al., 2005) where the ipsilateral front leg, the contralateral front leg and the

contralateral mesothoracic leg initiate turning for sounds presented at 90◦ and 180◦.

In order to summarize the steps combinations I gathered the occurrences where one,

two or three legs were simultaneously in the swing state (figure 4.14). Each occurrence

changed when a different leg combination appeared. It is important to note that in the

vast majority of cases two or three legs were never lifted offand touched the ground at

precisely the same time (cockroach; Hughes, 1952). Nevertheless, I considered these

cases as doublets or triplets.

In forward walking the majority of the stepping combinations occur in doublets,

with the following combinations: hind leg with opposite front leg, front leg with op-

posite middle leg and middle leg with opposite hind leg. In singlet combinations the

middle legs are most common. In triplets the front and hind legs of one side and the

middle leg of the other side represent the majority of cases.

During right and left turns there are only a few triplet combinations. Most cases

happened in singlets where the contralateral to the turn front and middle legs were

lifted off the ground. Most doublets represent successive opposite side leg combina-

tions as happened with the forward walking.

There are some step combinations that cannot be explained using the existing coor-

dination rules. For example ipsilateral neighbours shouldnot protract at the same time.

However this happened only once in forward walking and twiceduring turning. More

interestingly, contralateral neighbours were more often protracting at the same time.

For instance, there were 25 and 9 cases of both middle legs lift off the ground during

forward walking and turning, which also violates the standard coordination rules. In

4.3. Leg Coordination 83

Figure 4.14: Stepping combinations during forward walking (top), right turn (bottom left)

and left turn (bottom right). The dots in each case represent legs in swing state.


L FR MR HR FL ML HL

FR 1 21 129 192 71 11

MR 255 2 7 13 82 63

HR 6 179 0 147 17 75

FL 147 102 29 1 43 120

ML 23 56 96 246 0 6

HL 172 11 44 14 181 0

L FR MR HR FL ML HL

FR 0.24 4.94 30.35 45.18 16.71 2.59

MR 60.43 0.47 1.66 3.08 19.43 14.93

HR 1.42 42.22 0.00 34.67 4.01 17.69

FL 33.26 23.08 6.56 0.23 9.73 27.15

ML 5.39 13.11 22.48 57.61 0.00 1.41

HL 40.76 2.61 10.43 3.32 42.89 0.00

Table 4.6: Total number and probability of next steps during forward walking. Left col-

umn indicates the leg in swing before and top row the leg in swing after.

L FR MR HR FL ML HL

FR 1 4 7 12 31 19

MR 2 0 6 4 6 4

HR 14 2 2 13 6 14

FL 40 7 11 3 5 17

ML 4 3 9 52 2 2

HL 10 2 2 0 40 2

L FR MR HR FL ML HL

FR 1.35 5.41 9.46 16.22 41.89 25.68

MR 9.09 0.00 27.27 18.18 27.27 18.18

HR 27.45 3.92 3.92 25.49 11.76 27.45

FL 48.19 8.43 13.25 3.61 6.02 20.48

ML 5.56 4.17 12.50 72.22 2.78 2.78

HL 17.86 3.57 3.57 0.00 71.43 3.57

Table 4.7: Total number and probability of next steps during right turn. Left column

indicates the leg in swing before and top row the leg in swing after.

particular most of these cases happened towards the end of the turn or the beginning of

forward walking. One possible explanation for this is that in these cases both middle

legs were extending back reaching almost the limits of theirThCγ joints. This means

that if only one of the legs was lifted of the ground and the insect moved, the other leg

could not support the insect as it would have to extend to its full length.

In order to investigate further the influence of each leg to the other legs, I gathered

the cases of what is the first leg that enters the swing mode after one leg goes back to

its stance mode, for forward walking, right turn and left turn (tables 4.6 - 4.8). These

tables provide the number of occurrences and the resulting probability of each leg

transitioning to swing state after the other leg transitioned to stance mode.

From all the cases it is clear that the each leg almost never lifts up again before

another leg during forward walking and turning. During forward walking the front

right leg influences mostly the hind right, front left and middle left legs. The middle

right leg affects mostly the front right leg, the middle leftleg and the hind left leg. The

hind right leg affects mostly the middle right leg, the frontleft leg and the hind left leg.

4.4. Angles Relative to Speaker 85

L FR MR HR FL ML HL

FR 3 6 24 27 10 6

MR 41 2 4 5 5 17

HR 3 36 1 11 7 3

FL 8 31 13 1 7 10

ML 7 11 6 4 0 7

HL 16 7 16 8 6 0

L FR MR HR FL ML HL

FR 3.95 7.89 31.58 35.53 13.16 7.89

MR 55.41 2.70 5.41 6.76 6.76 22.97

HR 4.92 59.02 1.64 18.03 11.48 4.92

FL 11.43 44.29 18.57 1.43 10.00 14.29

ML 20.00 31.43 17.14 11.43 0.00 20.00

HL 30.19 13.21 30.19 15.09 11.32 0.00

Table 4.8: Total number and probability of next steps during left turn. Left column

indicates the leg in swing before and top row the leg in swing after.

The effect of the left legs is opposite as expected. During right turns the front right

leg influences mostly the front left, middle left and hind left legs. The middle right leg

influences mostly the hind right, front left, middle left andhind left legs. The hind right

leg influences mostly the front right, front left, middle left and hind left legs. Again

these results do not point towards simple coordination rules in which ground contact

of one leg causes transition to swing in a neighbouring leg.

4.4 Angles Relative to Speaker


• What are the body and ear angles relative to the sound source?

• At what angle to the sound do crickets stop their initial turnand start walking

forward?

• What deviations during forward walking lead to corrections in turning?

Because it was impossible to track the position of the spiracles and the ears, even

by using hand digitization, I used a 150 mm digital calliper (Resolution 0.01 mm, Tool-

zone) to do the necessary measurements after the experiments. Firstly the cricket was

sacrificed after experimentation by placing it in a freezer for approximately 30 min-

utes. The cricket was then removed from the freezer and allowed regain environment

temperature. After that, I used the calliper to measure the distance of the ears from

the front FTi joints. This was measured as 1.0±0.1 mm for all 16 specimens. The

position of the spiracles was approximated by measuring thedistances of three succes-

sive rotations following the model order of rotations from the mesothorax joint. These


measurements were stored at a vector where x = 2.5 mm, y = 2.0 mmand z=0.5 mm,

where all the rotations are 90◦.

I calculated three types of angles relative to the speaker (figure 4.16). I took the

2D projection to the ground of the 3D coordinates of each point of interest. The first

is the angle between the metathorax, the mesothorax and the centre of the speaker.

The second is the angle between the mesothorax, the prothorax and the centre of the

speaker. For the third angle I created a segment connecting the two ears. I then calcu-

lated the centre of the segment and created a second segment vertical to the previous

one. Therefore the third angle I calculated is the one between the tip of the second

segment, the middle of the ears’s segment and the centre of the speaker (see figure

4.16). Note that this is actually the most relevant measure but almost never reported

in previous studies. 180◦ is the angle where the cricket is perfectly aligned with the

speaker. There are cases when the cricket turned to the otherdirection than it should

but eventually finished the turn oriented towards the speaker. Therefore there are cases

where the angle relative to the speaker are less than 0◦ or more than 360◦. Table 4.9

summarizes the results and figure 4.15 depicts the results ofthe ears - speaker angle

in two examples. In the first the cricket finished its turn veryprecisely while in the

other the insect continued turning after crossing the alignment with the speaker. Note

that these are two different specimens. Some of the cricketswere able to track the

sound source more accurately than others. Note also that theoscillations observed in

the angles are due to the stepping cycles of the front legs.

Maximum difference with the speaker alignment at the beginning of the turn was

224.48◦ for the metathorax-mesothorax-speaker angle, 217.77◦ for the mesothorax-

prothorax-speaker angle and 217.36◦ for the ears-speaker angle. Minimum difference

with the speaker alignment at the beginning of the turn was 32.69◦ for the metathorax-

mesothorax-speaker angle, 28.91◦ for the mesothorax-prothorax-speaker angle and

37.73◦ for the ears-speaker angle. Maximum difference with the speaker alignment at

the end of the turn was 73.07◦ for the metathorax-mesothorax-speaker angle, 67.12◦ for

the mesothorax-prothorax-speaker angle and 80.10◦ for the ears-speaker angle. Min-

imum difference with the speaker alignment at the beginningof the turn was 0.58◦

for the metathorax-mesothorax-speaker angle, 0.31◦ for the mesothorax-prothorax-

speaker angle and 0.49◦ for the ears-speaker angle. Mean difference with the speaker

alignment at the beginning of the turn was 132.89◦ ±43.85◦ for the metathorax-mesothorax-

speaker angle, 131.49◦ ±44.90◦ for the mesothorax-prothorax-speaker angle and 130.73◦

±43.80◦ for the ears-speaker angle. Mean difference with the speaker alignment at the


end of the turn was 17.31◦ ±17.37◦ for the metathorax-mesothorax-speaker angle,

17.07◦ ±17.47◦ for the mesothorax-prothorax-speaker angle and 19.91◦ ±19.45◦ for

the ears-speaker angle.

In order to investigate the change of direction during forward walking I gathered

the peaks of the angles relative to the speaker during forward walking from all the

16 experiments (figure 4.17). I used MATLAB’s findpeaks function with minimum

time difference between successive peaks equal to 1 second.Maximum value of an-

gle difference for the mesothorax-metathorax-speaker was53.49◦, for the mesothorax-

prothorax-speaker was 62.57◦ and for the ears-speaker was 82.86◦. Minimum value of

angle difference for the mesothorax-metathorax-speaker was 0.57◦, for the mesothorax-

prothorax-speaker was 0.26◦ and for the ears-speaker was 0.34◦. Mean value of angle

difference was 16.24◦ ±11.62◦ for mesothorax-metathorax-speaker, 18.34◦ ±13.54◦

for mesothorax-prothorax-speaker and 28.48◦ ±16.04◦ for the ears-speaker. The re-

sults were summarized by using histograms with a bin size of 5◦ (figure 4.17).

Overall the results show that the crickets after the initialturn approach the call-

ing song meandering around the straight target direction. Earlier studies on a Kramer

treadmill resulted in deviation from the straight path of the insect around 30◦-60◦ (We-

ber et al. 1981,Gryllus campestris; Schmitz et al. 1982). Here I showed that most

of these deviations were lower than 30◦. One possible reason for this is that forces

generated while walking on a Kramer treadmill are opposing the acceleration of the

insect, thus altering sensory feedback. More recently it was showed that crickets can

discriminate sound deviating by only 1◦ (Schoneich and Hedwig, 2010). However,

thosse experiments were conducted on a trackball where the moving animal did not

alter the orientation and direction towards the sound and sound was constantly per-

ceived under identical conditions. In noisier environments such as in the arena this

precision in tracking the sound is probably less accurate. Another difference with pre-

vious studies is that there is course correction while the cricket is moving in contrast

to results from other species (Scapsipedus marginatus; Murphey and Zaretsky 1972,

Teleogryllus oceanicus; Bailey and Thomson 1977) where there is course correction

after a stop.


C E S T MMSI MMSA MPSI MPSA VMSI VMSA

01 01 R R 331.04◦ 205.83◦ 330.35◦ 199.41◦ 335.33◦ 190.75◦

01 02 L R 212.71◦ 186.63◦ 207.81◦ 182.98◦ 217.02◦ 187.10◦

02 01 L L 23.68◦ 180.25◦ 19.86◦ 191.76◦ 20.41◦ 181.81◦

02 02 R L 38.52◦ 161.11◦ 49.24◦ 175.49◦ 45.75◦ 189.49◦

03 01 R R 259.38◦ 204.38◦ 261.22◦ 191.49◦ 249.80◦ 189.62◦

03 02 L R 252.81◦ 183.46◦ 253.65◦ 173.93◦ 256.82◦ 174.33◦

04 01 R R 282.41◦ 196.66◦ 265.57◦ 179.13◦ 263.38◦ 167.21◦

04 02 L L 97.75◦ 161.79◦ 92.03◦ 166.09◦ 89.23◦ 170.18◦

05 01 R R 365.33◦ 197.96◦ 365.57◦ 193.73◦ 353.21◦ 202.89◦

05 02 L L -12.39◦ 177.89◦ -17.57◦ 194.64◦ -8.96◦ 195.53◦

06 01 R R 351.13◦ 137.42◦ 356.51◦ 123.56◦ 355.09◦ 126.87◦

06 02 L L 56.52◦ 159.06◦ 66.86◦ 173.47◦ 74.86◦ 168.32◦

07 01 R L 22.46◦ 110.63◦ 33.12◦ 118.13◦ 27.40◦ 99.94◦

07 02 L R 282.30◦ 177.01◦ 279.43◦ 170.63◦ 279.98◦ 183.55◦

08 01 R R 346.29◦ 169.47◦ 354.31◦ 162.93◦ 361.91◦ 146.01◦

08 02 L R 337.23◦ 195.25◦ 337.97◦ 176.37◦ 332.04◦ 168.49◦

09 01 L R 328.70◦ 166.83◦ 332.55◦ 157.40◦ 327.59◦ 160.58◦

09 02 R L -4.21◦ 169.90◦ 2.76◦ 180.88◦ 0.48◦ 191.80◦

10 01 L L 81.97◦ 201.03◦ 91.83◦ 211.73◦ 95.61◦ 215.75◦

10 02 R R 295.60◦ 197.56◦ 282.96◦ 181.21◦ 289.58◦ 179.43◦

11 01 R R 324.84◦ 169.42◦ 328.72◦ 157.67◦ 332.16◦ 149.78◦

11 02 R L 9.28◦ 183.47◦ 11.18◦ 191.19◦ 11.54◦ 192.51◦

12 01 R L 98.90◦ 178.82◦ 98.33◦ 185.10◦ 87.77◦ 181.23◦

12 02 R L -5.73◦ 192.85◦ -0.41◦ 205.46◦ 1.94◦ 219.33◦

13 01 L L 17.88◦ 158.71◦ 16.16◦ 160.12◦ 22.11◦ 145.57◦

13 02 R L 76.81◦ 213.81◦ 80.57◦ 222.67◦ 80.46◦ 227.09◦

14 01 R R 335.74◦ 177.34◦ 345.09◦ 170.90◦ 345.48◦ 174.74◦

14 02 R R 404.25◦ 201.30◦ 397.72◦ 193.10◦ 397.26◦ 200.10◦

15 01 R L 81.62◦ 181.11◦ 74.32◦ 181.41◦ 89.22◦ 175.36◦

15 02 L L 48.94◦ 107.91◦ 48.97◦ 112.37◦ 55.26◦ 118.08◦

16 01 L R 277.41◦ 190.98◦ 278.59◦ 182.57◦ 274.51◦ 189.19◦

16 02 R R 255.55◦ 168.72◦ 254.58◦ 168.54◦ 263.80◦ 178.98◦

Table 4.9: Angles properties for all the experiments. Cricket number (C), experiment

number (E), speaker(S), left (L), right (R), turn(T), metathorax - mesothorax - speaker

initial angle (MMSI) and after turn(MMSA), mesothorax - prothorax - speaker initial an-

gle (MPSI) and after turn (MPSA), vertical line ears point - middle ears point - speaker

initial angle (VMSA) and after turn (VMSA) and antennae and head movement frames

before first leg swing (AM).


Figure 4.15: Examples of angles between the ears and the speakers (S). Top figure

shows results from a cricket that tracked the sound very precisely. Bottom figure shows

results from a cricket that continued turning but corrected its course. Green lines indi-

cate the end of turn. Dashed red lines indicate a stop.


Figure 4.16: Summary of the angles of interest relative to the speakers (S). Top figure

summarizes the angles between metathorax, mesothorax and the speaker. Middle fig-

ure summarizes the angles between mesothorax, prothorax and the speaker. Bottom

figure summarizes the angles between the ears’ lines and the speaker. Right turns are

marked with red colour and left turns are marked with blue colour.


Figure 4.17: Angles before change of direction during forward walking. Number of

peaks in the metathorax-mesothorax-speaker angles (Top). Number of peaks in the

mesothorax-prothorax-speaker angles (Middle). Number of peaks in the ears-speaker

angles (Bottom).


4.5 Ears’ Input Estimation

In this section I address the following question:

• What is the sound input from each side of the cricket during phonotaxis?

In order to test the parameters of the estimation algorithm Ifirst created a simulated

stationary cricket setup. This is similar to the setup that was used to calculate the delays

and gains of transmitting the sound to real crickets (Michelsen et al., 1994). The gains

represent the change of amplitude and the delays in the phaseangles from the entrance

of the tracheal system to the surface of the tympanum.

I placed a simulated cricket in the middle of a circular arenaand rotated the speaker

around the right tympanum one degree every one millisecond (figure 4.18). In the

original paper the speaker was moved every 30◦. The distance from the speaker in

my simulation was 400mm. The lengths of the cricket segmentsare based on the

mean values from the estimated lengths from all the cricketsthat were tracked (table

4.10). The angle values are based on the forward walking stride percentage results that

were depicted in section 4.2. The distance between the two tympani was 12.08 mm,

the distance between each spiracle and the contralateral tympanum was 9.85 mm, the

distance between the each spiracle and the ipsilateral ear was 6.98 mm and the distance

between the two spiracles was 4 mm. For the calculations I consider that there is only

one tympanum per side and it is placed in the line connecting the FTi with the TiTa

joints.

For the calculation of the ear input I used all four sound inputs. These are the

ipsilateral tympanum (IT), the ipsilateral spiracle (IS),the contralateral spiracle (CS)

and the contralateral tympanum (CT). According to Michelsenet al. (1994) at car-

rier frequency of 4.5 kHz the contribution of the CT input to the sound is not signif-

icant. However, at 4.8 kHz, which is the carrier frequency I used in my experiments,

the CT input affects significantly the directionality. This is supported by experiments

where the contralateral tympanum was blocked (Boyd and Lewis, 1983). This fact was

not taken into account in previous 2D simulations of the model (Reeve et al., 2007).

The internal delays and the sound gains for each spiracle andtympani are based on

(Michelsen et al., 1994) and data provided by Axel Michelsen(personal communi-

cation). These values are presented in table 4.12. Because ofthe way these values

were originally calculated, I had to convert them before using them in the algorithm.

Excluding the IT internal delay, which is zero by default, the other values had to be

transformed by subtracting them from 180◦. If the value is negative then an addition of

4.5. Ears’ Input Estimation 93

Segment MV SD

Metahorax - mesothorax (0 - 1) 3.05 ±0.23

Mesothorax - thorax-coxa (1 - 2, 1 - 6) 1.86 ±0.11

Middle coxa (2 - 3, 6 - 7) 1.59 ±0.17

Middle femur (3 - 4, 7 - 8) 5.29 ±0.32

Middle tibia (4 - 5, 8 - 9) 4.33 ±0.22

Mesothorax - prothorax (1 - 10) 3.58 ±0.31

Prothorax - thorax - coxa (10 - 11, 10 - 15)1.56 ±0.16

Front coxa (11 - 12, 15 - 16) 2.01 ±0.34

Front femur (12 - 13, 16 - 17) 5.50 ±0.30

Front tibia (13 - 14, 17 - 18) 3.54 ±0.24

Metathorax - thorax - coxa (0 - 19, 0 - 23) 1.68 ±0.14

Hind coxa (19 - 20, 23 - 24) 1.82 ±0.17

Hind femur (20 - 21, 24 - 25) 10.50 ±0.44

Hind tibia (21 - 22, 25 - 26) 7.98 ±0.38

Table 4.10: Mean values (MV) and standard deviations (SD) for the lengths of body and

leg segments in millimetres.

360◦ needed to be made to make the values positive. Note that I usedthe same values

for all the crickets. According to Michelsen et al. (1994) the diffraction and time de-

lays did not vary much between individuals. In contrast, thegains between individuals

showed substantial variation. Furthermore, differences in the gains between the two

sides in the same cricket were found.

I did not take into account diffractions of the sound, as thiswas not possible to

calculate. Michelsen et al. (1994) estimated a diffractionof 1-2 dB. As I wanted to

estimate the input from an analogue signal I used a sample rate of 8×106 Hz instead of

44.1 kHz used in the experiments. By using this sample rate I then used the index of the

song to estimate the input by using the following procedure:first at every millisecond

the sound is broadcast at timet. Then the sound reaching each of the two tympani

and the spiracles must be at the timet − tp where thetp is the time taken to reach the

specific point from the sound source. To calculate this time Iused a speed of sound

time value of 344,384 m/s or mm/ms. This value is for an air temperature of 22◦C, the

average temperature during the arena experiments. Therefore the time taken for the

signal to travel was taken by the distance of the point from the sound source divided

by the speed of sound. Then, this value was added to the internal delays multiplied


Joint Parameters Values

Metathorax (0) x, y, z 0, 0, 0

Segment Parameters Values

Metahorax - mesothorax (0 - 1) α, β, γ 0◦, -10◦, 0◦

Middle right coxa (2 - 3) α, β, γ (ThC) -25◦, 30◦, 0◦

Middle right femur (3 - 4) β (CTr) -80◦

Middle right tibia (4 - 5) β (FTi) 95◦

Middle left coxa (6 - 7) α, β, γ (ThC) 25◦, 30◦, 0◦

Middle left femur (7 - 8) β (CTr) -80◦

Middle left tibia (8 - 9) β (FTi) 95◦

Mesothorax - prothorax (1 - 10) α, β, γ 0◦, -10◦, 0◦

Front right coxa (11 - 12) α, β, γ (ThC) -45◦, 80◦, -15◦

Front right femur (12 - 13) α (TrF), β (CTr) 45◦, -95

Front right tibia (13 - 14) β (FTi) 65

Front left coxa (15 - 16) α, β, γ (ThC) 45◦, 80◦, 15◦

Front left femur (16 - 17) α (TrF), β (CTr) −45◦, -95◦

Front left tibia (17 - 18) β (FTi) 65◦

Hind right coxa (19 - 20) α, β, γ (ThC) -8◦, 30◦, -65◦

Hind right femur (20 - 21) β (CTr) -70◦

Hind right tibia (21 - 22) β (FTi) 130◦

Hind left coxa (23 - 24) α, β, γ (ThC) 8◦, 30◦, 65◦

Hind left femur (24 - 25) β (CTr) -70◦

Hind left tibia (25 - 26) β (FTi) 130◦

Table 4.11: The joints and segments parameters values used in the stationary cricket

setup. The units for the joint limits are in millimetres and for the angles in degrees.

Input Gain Delay 180 - delay Final delay

IT 1.0 0◦ 0◦ 0◦

IS -1.44 135◦ 45◦ 45◦

CS -0.54 230◦ -50◦ 130◦

CT -0.23 270◦ -90◦ 90◦

Table 4.12: Transmission gains and internal delays of the four sound inputs. Ipsilat-

eral tympanum (IT), ipsilateral spiracle (IS), contralateral spiracle (CS) and contralateral

tympanum (CT). The internal delays represent the phase difference in the time of the

sound arrival as it travels inside the tracheal tubes.


by the degree of sound change which is given by 1360×carrierFrequency× 1000. Then

from all the samples I took the maximum value, which represents the size of tympanal

vibration. The results of this simulation are provided in figure 4.18 and show the clear

directionality of the sound for both sides as the speaker is rotated around the cricket.

Before applying the algorithm to the data I also investigatedthe estimation of the

ears input in the stationary setup while the insect is movingits front legs (figure 4.19).

For the simulation I used the data gathered for the step cycleas presented in section

4.2. Each frame corresponds to 3 ms. I chose the position of the speaker to be where

the maximum difference was observed in the previous experiment (270◦). Because

in this simulation the legs were moving, I placed the speakerat 270◦ relative to the

metathorax position. First, I calculated the difference between the two sides when the

simulated cricket walked forward. When the front right leg initially performs its swing

there is a decrease in the difference of the two sides and thenan increase as the leg

transitions to the stance and the front left leg to its swing.The next case was when

the legs performed a left turn and therefore moving away fromthe speaker. There

is a decrease in the difference as the front right leg is in swing, an increase before

decreasing again as the leg changes into stance and finally anincrease as the front

left leg enters its stance. The last case was when the legs would perform a right turn.

Initially, there would be an increase as the right leg would move closer to the sound

source and a decrease as it would go back to stance, coupled with an increase as the

front left leg would perform a swing and a decrease when it would finish its swing.

From all the above cases it is obvious that during a step cyclethe ears input on the two

sides is not constant but instead significantly changed. It important to note that only

the legs are moving at this situation and not the body, which means that this experiment

resembles more the case when the cricket is walking on a trackball.

If the sound is placed directly in front of the cricket there is smaller difference be-

tween the two sides (figure 4.20). During the swing phase of each leg the difference

reaches up to 3 dB, while during stance it reaches up to 4 dB. Thissuggests that the

cricket should ignore differences of 4 dB. Alternatively thecricket may be influenced

by the change of input during step,e.g it could detect that there is an increasing dif-

ference during ipsilateral swing if turning to the wrong way(figure 4.19 top, middle),

whereas during a turn towards the sound the difference decreases sharply during the

swing (figure 4.19 bottom).

In order to calculate the two sides input from the video recordings I first resampled

the metathorax coordinates and the angles of all joints from3.33 ms to 1 ms using


−3 dB

−8 dB

−13 dB

−18 dB

−23 dB

0°

30°

60°

90°

120°

150°180°

210°

240°

270°

300°

330°

Figure 4.18: Simulation of sound directionality for a stationary cricket. The artificial

cricket is placed in the middle of the arena (top). The sound source is rotated around

the right tympanum every 1◦. The algorithm calculates the values of the sound input for

the left (blue) and the right (red) ear. Directional pattern of ear values with respect to

the 0◦ value is presented (bottom).

cubic spline interpolation. Then, I regenerated the coordinates of all the joints, the

ears and the spiracles using the same rotation order that I used during the tracking


Figure 4.19: Single step cycle decibel difference between the two sides in three different

cases. During forward walking (top), during left turn (middle) and during right turn

(bottom). The arrow indicates the direction of the sound. Rectangles represent swing

phases for front right (light red) and left (light blue) legs.


Figure 4.20: Single step cycle decibel difference between the two sides during forward

walking. The arrow indicates the direction of the sound. Rectangles represent swing

phases for front right (light red) and left (light blue) legs.

procedure. Because the original results were based on 300 fpsvideo recordings, I had

to find the delay of the cricket song when the coordinates wereresampled. In order

to do that I cross correlated the two signals using the MATLABxcorr function. This

function outputs the delay between the two signals and therefore I was able to extract

the initial song position at the first frame. Then for every millisecond I calculated the

distance from the centre of the speaker. The centre of the left speaker was in X: 290

mm, Y: 1200 mm and Z: 100 mm and the right speaker at X: 290 mm, Y:0mm and

Z: 100 mm. These measurements were done by using a ruler. Figures 4.21 and 4.22

depict typical examples of the estimation of ears input.


Figure 4.21: Examples of ear’s input estimation. Top figure shows results from a cricket

that tracked the sound very precisely. Bottom figure shows results from a cricket that

continued turning but corrected its course.


Figure 4.22: Example of ear’s input estimation during turn. The cricket performed a left

turn towards the left speaker.

Finally, I summarized the results by calculating the maximum input value be-

fore the angles’ peaks presented in the previous section (figure 4.23). I used a pe-

riod of 300 ms based on the step cycle of the insect but shifted50 ms before the

peak as this is the estimated time that the cricket takes to process the sound input

through the brain (Baden and Hedwig, 2008). Maximum value of decibel difference

for the mesothorax-metathorax-speaker was 15.83 dB, for themesothorax-prothorax-

speaker was 22.21 dB and for the ears-speaker was 18.11 dB. Minimum value of angle

difference for the mesothorax-metathorax-speaker was 1.04 dB, for the mesothorax-

prothorax-speaker was 0.69 dB and for the ears-speaker was 0.53 dB. Mean value of

decibel difference was 6.06±3.39 dB for mesothorax-metathorax-speaker, 6.47±3.95

dB for mesothorax-prothorax-speaker and 5.85±3.54 dB for the ears-speaker. Maxi-

mum value of decibel difference before the transition from turning to forward walking

was 19.17 dB and minimum difference was 7.76 dB. Mean value of decibel difference

was 13.17 dB± 8.5 dB. These results suggest that the crickets require a difference

of 3-5 dB to cause a course correction, which is far from precise and that quite large

differences can be tolerated.


Figure 4.23: Decibel values in the angles peaks before change of direction during for-

ward walking. Number of peaks in the metathorax-mesothorax-speaker angles (Top).

Number of peaks in the mesothorax-prothorax-speaker angles (Middle). Number of

peaks in the ears-speaker angles (Bottom).

Chapter 5

Discussion

5.1 Introduction

Insect walking requires the coordination of multiple joints in a single leg to produce

various stepping patterns, the coordination of all the legsto produce various gaits and

the interaction of the motor output with various sensory inputs and commands from

the brain or the other ganglia. The work in this thesis was motivated by the possible

interactions between sensory input and motor output in insects. I chose to study cricket

phonotactic behaviour as such an example. Previous studieshad focused more on the

auditory input processing or provided partial informationabout the body and leg move-

ments. I posed questions derived from the current literature (chapter 2) and I created

new tools to obtain data about this behaviour (chapter 3). Furthermore, I provided new

information about single leg motion, leg coordination, body angles relative to a sound

source and an estimation of the ear’s input (chapter 4).

In this chapter, I complete my thesis by discussing the outcome. First, I summarize

the contributions I made, as presented in the chapters 3 and 4(section 5.2). I then

suggest some of the future work that could be based on my thesis (section 5.3). Finally,

I conclude my thesis with the closing remarks (section 5.4).

5.2 Contributions

• In chapter 3 I presented a new method for studying cricket phonotactic behaviour

that produces data on the complete kinematic motions of a freely walking cricket

responding to calling song. As well as being novel for crickets, the methods

presented here extend the state-of-the-art in insect kinematic analysis in several

103

104 Chapter 5. Discussion

ways. The level of detail (full 3D information on all leg joints) has previously

been obtained only for tethered animals, of a larger size, with body geometries

more amenable to viewing all joints. Moreover I obtain additional information

on body articulation, pitch and roll. This is produced by a semi-automated track-

ing system that compares favourably to that described in Bender et al. (2010)

both in terms of overall fitting error and in terms of the rate of user correction

required (1-2% of frames vs. 3-5% of frames reported in Benderet al. (2010)).

Furthermore, since I avoid using inverse kinematics, I needto mark fewer points

in the insect. Marking more points on the legs would be very difficult, if not

impossible. The total cost of the experimental setup, including the highspeed

cameras (but not the computer) totalled less than £2000. This method allowed

me to investigate further the role of each leg pair, each leg and each joint in the

walking activity.

• In section 4.2 I presented for the first time in detail the movements of most of

the leg and body joints during phonotaxis. Earlier studies provided information

either only for the forward walking in the absence of auditory stimulus (Lak-

sanacharoen et al., 2000) or limited information about kinematics in cases where

the animal is restricted (Baden and Hedwig, 2008; Witney and Hedwig, 2011).

Results from my study include the movement of each joint during right and left

turns and forward walking. Some joints contributed more to the change in the

patterns of step of each leg. For instance the ThC joints of the front legs play

an important role during contralateral turning, while the CTr and FTi joints con-

tribute more during ipsilateral turning. The middle leg’s TiTa position is used

as a centre for rotation for the insect. Furthermore, I showed that the 3 DoF

of the ThC joint and motion of body joints are important for the movement of

the cricket, which is something that existing models of insect walking generally

neglect in their implementation.

• In section 4.3 I presented for the first time the leg coordination results from the

crickets while they performed phonotaxis. Although most ofthe results agree

with existing coordination rules derived from stick insectwalking (Cruse et al.,

1991), there were some cases that cannot be explained and therefore modifica-

tion or an alternative methodology has to be followed. For example there were

cases when both middle legs were lifted off the ground. Furthermore, I found

which legs perform a swing after the transition to the stancephase of another

5.2. Contributions 105

leg. Context-dependent changes in leg coordination mechanisms have been sug-

gested previously for stick insects (Blasing and Cruse, 2004; Durr, 2005; Rosano

and Webb, 2007). For example coupling strength for certain rules between the

two sides might differ during turning for ipsilateral and contralateral to the turn

legs. Further analysis of the results using the methodologydiscussed in Durr

(2005) could provide more information about specific modifications of the rules.

• In section 4.4 I presented the analysis of the body angles relative to the speaker.

These show similar but somewhat smaller deviations from straight walking than

previous reports (Weber et al., 1981). For the first time I calculated the angles

between the speaker and the ears. This increases the effective angle to sound

by 10◦-15◦ compared to the other two body angles. The accuracy of the sound

source tracking differs compared to recent experiments on atrackball (Schoneich

and Hedwig, 2010). I should note here that the speed of the cricket in these

experiments was lower than the speed reported for experiments on a trackball.

Previous work has shown that there might be a rapid steering response in every

sound pulse when it is presented from alternate directions (Hedwig and Poulet,

2004). One possible reason for this is that depending on the speed of the cricket

the local connection between the auditory circuit and the front legs might change

its effect. For lower speeds the commands from the brain might play more im-

portant role in walking (for instance during initiation of turning, the antennae and

the head are the first to move). For higher speeds this local connection might af-

fect more the walking behaviour and therefore contribute tothis rapid steering

change with reduced involvement of the brain neurons.

• In section 4.5 I estimated for the first time the sound input onboth sides as the

insect performed phonotaxis, based on the precise positionof their ears in the

sound field. I also implemented a simulation to estimate the ears input as the

insect would walk on a trackball. The results show that the cricket could ex-

perience auditory inputs that differ by as much as 4 dB duringits step cycle

to sound ahead and by as much as 7-8 dB when turning to sound on one side.

Comparing ear inputs to corrections in heading suggests a difference of 3-5 dB

is needed to cause the animal to correct its course. For the above calculations I

used the same delay and gain values for the four sound inputs during the entire

walking sequence. Initial data from experiments on a trackball suggest that pos-

sible changes in the membrane of the spiracles as the cricketwalks might affect


the sound input (Kostarakos et al., 2009). Therefore a possible mechanical con-

nection between the spiracles and the front legs might affect the sound input on

the two sides. Another possibility is that there is a neural connection from the

sensors of the legs that inhibits the auditory circuit connection to the front legs

as described in Baden and Hedwig (2008). This could happen forexample in

some cases when during a step the sound difference initiallybecomes larger as

the insect moves one of the front legs (figure 4.21).

5.3 Future Research

5.3.1 Experiments

There are several possible improvements or extensions thatcould be made to this sys-

tem. During the experiments the cameras are moved manually to follow the insect.

It could be possible to motorise this movement, using a thirdcamera that does online

tracking of the body position (as one blob) of the insect in the arena. Such systems have

already been successfully implemented in our lab, for example, to track flies (Stewart

et al., 2010). However, this might cause additional noise and affect the insect’s be-

haviour. Currently the manual tracking needs to ensure the insect does not cover all

visible grid points. However, with a motorised system or using distance measuring

sensors attached to the cameras, their position could be obtained automatically, which

would avoid completely the need for grid marking and tracking. This would be also

useful with cameras that do not have large recording memory.

Although I have tried to make the visual environment fairly uniform (white sound

proofing, white covers on the speakers) nevertheless it is possible that the cricket in this

setup could be combining visual tracking or visual stabilisation with its phonotactic re-

sponse (Payne et al., 2010). Experiments with infrared light resulted in worse image

quality and made the tracking much less reliable. Although some previous reports

suggests visual stimuli strongly influence tracking behaviour on the Kramer treadmill

(Weber et al., 1981), recent results have not detected dramatic differences in arena

tracks produced in the light and the dark (Payne, 2010). It would nevertheless be inter-

esting in this setup to measure more precisely whether phonotaxis tracks are altered in

a high contrast visual environment, or if introducing a single visual object elicits visual

tracking in competition with tracking of the sound.

Other possible experiments could include the tracking of the abdomen and the end

5.3. Future Research 107

of the tarsus, since the middle section of the tarsus is the one actually touching the

ground in the front and middle legs as the insect moves. However, tracking the tar-

sus would probably need hand digitization because its smallsize does not allow it

to be painted. Furthermore, tracking the antennae positions through 3D space could

give more information about the movements of these sensors and the combination of

the two sensory modalities. I only presented sound from one direction in these ex-

periments but it would be interesting to observe the change in the movements of the

cricket as it changes its direction. Preliminary results showed that some crickets re-

sponded immediately to the change of sound direction, whileothers stopped when the

sound direction changed. Finally, experiments with one of the tympani blocked could

show the response or not of the cricket to the sound stimulation.

I only worked on behavioural experiments with the current setup, but in the future

it might be possible to combine it with electromyography (Delcomyn and Usherwood,

1973; Watson and Ritzmann, 1997; Sponberg and Full, 2008) or neuron recordings

(Pearson, 1972; Takeuchi and Shimoyama, 2004; Keller et al., 2007; Dupuy, 2009).

This will offer more insights in the internal functionalityof the insect, but may require

more precise synchronisation of the cameras. The results presented here indicate which

joint angles contribute most to changing leg motion during turns (see section 4.2).

Therefore, they suggest which muscles or motor neurons might be best for such a

paradigm. Some work towards this direction has been carriedout while the cricket

walked on a trackball (Baden and Hedwig, 2008).

Finally, it would be ideal to combine methods in order to givemore information

for each individual cricket. For instance a cricket could first be tested in the arena as

presented here, then on a slippery surface (Gruhn et al., 2006; Bender et al., 2010)

and a stationary setup for estimating the gains and the delays for the ears (Michelsen

et al., 1994). Doing experiments on these setups would give more information on each

individual cricket. A slippery surface setup could providemore information on single

leg movement, since it decouples the leg relative to the ground. Also, amputations and

neurophysiology experiments are easier to perform for a fixed animal. Furthermore,

when I estimated the ear’s input I assumed that the state of the spiracles remains un-

changed during a step cycle. However, the spiracles have a membrane in front of them

which is held open with wax during stationary experiments (Michelsen et al., 1994).

Maybe a camera zoomed in the specific region could show the state of the spiracle

before the insect starts moving and during walking (Kostarakos et al., 2009).


5.3.2 Software

Possible improvements of the software could include a cross-platform version, for ex-

ample by using the Qt libraries (http://qt.nokia.com/). Furthermore, the performance

(i.e. processing speed) of the tracking procedure would increaseif we integrate it with

more powerful graphics processor unit (GPU) functionality. The current version of

OpenCV includes an experimental GPU functionality. The use of an optimization al-

gorithm that runs in a GPU would also improve significantly the performance of the

model fitting procedure which is the most time-consuming.

Another approach to track the movements of a person has been recently developed

(Shotton et al., 2011). It takes into account the possible configurations of the joints of

a moving person and trains an algorithm to match each configuration to a given depth

map. Since now I know the possible values of the joint of an insect it could be possible

to follow a similar approach to track the insect. This would further ease the use of

electromyography because it would not be necessary anymoreto paint the points of

interest on the insect.

5.3.3 Modelling

Based on the results from this work it is possible to extend existing models of cricket

phonotaxis, which could be run on a simulated hexapod robot.In order to achieve

that the model would have to be able to control joint coordination in the single leg,

coordination between all six legs and integrate a neural circuit for auditory process-

ing. Implementations of the auditory circuit exist from previous studies (Webb, 2006,

2008).

First, I would start by modelling a single leg controller. Obviously there would be

differences between the three leg pairs. Since there is no detailed information about the

neural control of walking in crickets, I would have to base the modelling in current stick

insect and cockroach models of locomotion. One possible modelling approach of the

single leg controller is the use of individual pattern generators for each joint that are af-

fected by neighbouring joint properties such as the joint angle and load (Ekeberg et al.,

2004). Since neurophysiological information is very limited about crickets, I would

have to hypothesize possible neural connections between different joints. Parameters

to the model could be the angular speed, range of motion and load information. The leg

structure should represent as accurately as possible the cricket morphology and there-

fore include the dimensions of the body and leg segments of the real crickets provided

5.3. Future Research 109

in table 4.10. Previous models have used only 3 DoF in total for each leg. How-

ever, in section 4.2 I demonstrated that all 5 DoF are important for the leg movements.

Therefore, an extension of the models need to incorporate these changes. One recent

example of such extensions is a 4 DoF cockroach middle leg model (Doorly, 2011).

The simulation could be tested first in a kinematic simulation and later on a dynamic

simulator such as using Open Dynamics Engine (ODE - www.ode.org), that has been

used in earlier studies (Rosano and Webb, 2007). This approach could show the pos-

sible implications of the model interacting with the real world properties. The model

should not only generate stable walking patterns, but approximate the leg kinematics

of the crickets. The evaluation should include leg trajectories and joint angle values.

In order to alter the motion to produce inside or outside turning patterns a change in

the effects to the joints should be made. For instance the middle leg CTr joint alters its

direction of movements during stance and swing. In phonotaxis, these changes are the

output of the auditory processing circuit. It has been suggested from previous trackball

results that crickets make a rapid change in heading to everysound pulse (Hedwig and

Poulet, 2004). However, as there is no coordination of step cycle with sound pattern,

this would imply that the animal must make a very specific adjustment according to the

current position of the leg. The front leg effects shown in figure 4.11 are interesting in

this regard. If one assumed a sound pulse louder on one side inhibits the ipsilateral ThC

joints and contralateral CTr and FTi joints, this could produce the observed change in

step motion.

Variations of Cruse’s rules (Cruse et al., 1991) have been usedin many studies (Es-

penschied et al., 1993, 1996; Rosano and Webb, 2007; Lewingerand Quinn, 2008). It

might be possible to adjust some of the rules to generate gaits similar to the crickets.

However, as presented in section 4.2 stance-swing transitions might involve more than

FTi extension-flexion transitions. Recent studies regarding leg coordination in stick in-

sects have shown that the movement of one leg can affect the activity of the ThC joints

in other two ipsilateral legs (Borgmann et al., 2007, 2009, 2012). Initial simulations

have been recently published (Daun-Gruhn and Buschges, 2011; Daun-Gruhn et al.,

2012). This further extends the single leg controller I mentioned above to coordinating

the three ipsilateral legs. However, more studies will be needed to follow this approach

for all six-legs coordination.


5.3.4 Robot Implementation

A robotic model could be used to further understand this biological system (Webb,

2000, 2001, 2006). This will be a neuromechanical model of the cricket, which will

be based on a hardware model (physical structure) and a software model (control algo-

rithm). For this purpose, I would have to overcome several technical challenges.

The robot should be designed to represent as accurately as possible cricket mor-

phology, but would need to be on a different scale due to the size of the required mo-

tors. The motors should have position and current (load) feedback, such as Dynamixel

MX-28 and MX-64 servos (www.robotis.com). Initially, a single leg could be tested.

Making a 3 DoFe ThC joint that could handle the weight of the robot would be one of

the challenges of the project.

Then the two front legs with a circuit equipped with microphones that simulates

the cricket auditory system could be integrated. The noise from the motors would

probably affect the input of the microphone sensors. Also the distance between the

two FTi joints of the two front legs would be different than the real cricket. Therefore

placing microphones on each leg would not create the same system as in crickets. One

possible solution would be to place the circuit board responsible for phonotaxis in the

front part of the robot similar to (Horchler et al., 2004). However, this configuration

would not allow the ears to move as each of the two front legs moves. Alternatively, I

could use lower sound frequency for the calling song.

5.4 Conclusion

Clearly there remain a number of technical and algorithmic challenges to be solved

before a complete hexapod robot model of cricket phonotaxiscould be built. Never-

theless, this thesis provides essential information towards closing the loop between the

sensory inputs and motor outputs in this behaviour. By highlighting the key leg motion

changes involved in phonotactic turns it points the way for future experimental and

simulation work.

Bibliography

Aggarwal, J. K. and Cai, Q. (2002). Human motion analysis: A review. In Nonrigid

and Articulated Motion Workshop, 1997. Proceedings., IEEE, pages 90–102.

Akay, T., Haehn, S., Schmitz, J., and Buschges, A. (2004). Signals from load sensors

underlie interjoint coordination during stepping movements of the stick insect leg.

Journal of Neurophysiology, 92(1):42–51.

Allen, T. J., Quinn, R. D., Bachmann, R. J., and Ritzmann, R. E. (2003). Abstracted bi-

ological principles applied with reduced actuation improve mobility of legged vehi-

cles. In2003 IEEE/RSJ International Conference on Intelligent Robots and Systems,

2003. (IROS 2003). Proceedings, volume 2, pages 1370–1375.

Altman, J. and Kien, J. (1987). Functional organization of the subesophageal ganglion

in arthropods. In Gupta, A., editor,Arthropod brain: its evolution, development,

structure and function, pages 265–301. Wiley, New York.

Atkins, G., Chiba, A., Atkins, S., and Stout, J. F. (1988). Low-pass filtering of sound

signals by a high-frequency brain neuron and its input in thecricketAcheta domes-

tica l. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and

Behavioral Physiology, 164(2):269–276.

Autrum, H. (1958). Electrophysiological analysis of the visual systems in insects.

Experimental Cell Research, 14(5):426–439.

Baden, T. and Hedwig, B. (2007). Neurite-specific Ca2+ dynamics underlying sound

processing in an auditory interneurone.Developmental Neurobiology, 67(1):68–80.

Baden, T. and Hedwig, B. (2008). Front leg movements and tibialmotoneurons un-

derlying auditory steering in the cricket(Gryllus bimaculatusde Geer).Journal of

Experimental Biology, 211(13):2123–2133.

111

112 Bibliography

Bailey, W. J. and Thomson, P. (1977). Acoustic orientation inthe cricketTeleogryllus

oceanicus(Le Guillou). Journal of Experimental Biology, 67(1):61–75.

Ball, E. E., Oldfield, B. P., and Rudolph, K. M. (1989). Auditory organ structure,

development, and function. In Huber, F., Moore, T., and Lohrer, W., editors,Cricket

Behavior and Neurobiology, pages 391–422. Cornel University Press, Ithaca, NY,

US.

Barai, R. K. and Nonami, K. (2007). Optimal two-degree-of-freedom fuzzy control for

locomotion control of a hydraulically actuated hexapod robot. Information Sciences,

177(8):1892–1915.

Bassler, U. (1988). Functional principles of pattern generation for walking movements

of stick insect forelegs: The role of the femoral chordotonal organ afferences.Jour-

nal of Experimental Biology, 136(1):125–147.

Bassler, U. (1993). The femur-tibia control system of stick insects – a model system for

the study of the neural basis of joint control.Brain Research Reviews, 18(2):207–

226.

Bassler, U. and Buschges, A. (1998). Pattern generation for stick insect walking

movements–multisensory control of a locomotor program.Brain Research. Brain

Research Reviews, 27(1):65–88.

Beer, R. D., Chiel, H. J., Quinn, R. D., Espenschied, K. S., and Larsson, P. (1992).

A distributed neural network architecture for hexapod robot locomotion. Neural

Computation, 4(3):356–365.

Beer, R. D., Chiel, H. J., and Sterling, L. S. (1989). Advances inneural information

processing systems 1. chapter Heterogeneous neural networks for adaptive behavior

in dynamic environments, pages 577–585.

Bell, W. J. and Kramer, E. (1979). Search and anemotactic orientation of cockroaches.

Journal of Insect Physiology, 25(8):631–640.

Bender, J. A., Simpson, E. M., and Ritzmann, R. E. (2010). Computer-assisted 3D

kinematic analysis of all leg joints in walking insects.PLoS ONE, 5(10):e13617.

Bender, J. A., Simpson, E. M., Tietz, B. R., Daltorio, K. A., Quinn, R. D., and Ritz-

mann, R. E. (2011). Kinematic and behavioral evidence for a distinction between

Bibliography 113

trotting and ambling gaits in the cockroachBlaberus discoidalis. Journal of Exper-

imental Biology, 214(12):2057–2064.

Bennet-Clark, H. (1989). Songs and the physics of sound production. In Huber, F.,

Moore, T., and Lohrer, W., editors,Cricket Behavior and Neurobiology, pages 227–

261. Cornel University Press, Ithaca, NY, US.

Bennet-Clark, H. C. (1975). The energetics of the jump of the locust schistocerca

gregaria.Journal of Experimental Biology, 63(1):53–83.

Berns, K., Cordes, S., and Ilg, W. (1994). Adaptive, neural control architecture for

the walking machine LAURON. InProceedings of the IEEE/RSJ/GI International

Conference on Intelligent Robots and Systems ’94. ’AdvancedRobotic Systems and

the Real World’, IROS ’94, volume 2, pages 1172–1177. IEEE.

Birch, M., Quinn, R., Hahm, G., Phillips, S., Drennan, B., Fife,A., Verma, H., and

Beer, R. (2000). Design of a cricket microrobot. InRobotics and Automation, 2000.

Proceedings. ICRA ’00. IEEE International Conference on, volume 2, pages 1109–

1114.

Blasing, B. and Cruse, H. (2004). Mechanisms of stick insect locomotion in a gap-

crossing paradigm.Journal of Comparative Physiology A: Sensory, Neural, and


Boggess, M. J., Schroer, R. T., Quinn, R. D., and Ritzmann, R. E. (2004). Mechanized

cockroach footpaths enable cockroach-like mobility. In2004 IEEE International

Conference on Robotics and Automation, 2004. Proceedings. ICRA ’04, volume 3,

pages 2871–2876.

Bohm, H. and Schildberger, K. (1992). Brain neurones involvedin the control of

walking in the cricketGryllus bimaculatus. Journal of Experimental Biology,

166(1):113–130.

Borgmann, A., Hooper, S. L., and Buschges, A. (2009). Sensory feedback induced by

front-leg stepping entrains the activity of central pattern generators in caudal seg-

ments of the stick insect walking system.The Journal of Neuroscience, 29(9):2972–

2983.

114 Bibliography

Borgmann, A., Scharstein, H., and Buschges, A. (2007). Intersegmental coordination:

Influence of a single walking leg on the neighboring segmentsin the stick insect

walking system.Journal of Neurophysiology, 98(3):1685–1696.

Borgmann, A., Toth, T. I., Gruhn, M., Daun-Gruhn, S., and Buschges, A. (2012).

Dominance of local sensory signals over inter-segmental effects in a motor system:

experiments.Biological cybernetics, pages 1–13.

Boyan, G. S. (1980). Auditory neurones in the brain of the cricketGryllus bimaculatus

(de Geer).Journal of Comparative Physiology A: Neuroethology, Sensory, Neural,

and Behavioral Physiology, 140(1):81–93.

Boyan, G. S. (1998). Development of the insect auditory system. In Hoy, R. R.,

Popper, A. N., Fay, R. R., Fay, R. R., and Popper, A. N., editors,Comparative

Hearing: Insects, volume 10 ofSpringer Handbook of Auditory Research, pages

97–138. Springer New York.

Boyan, G. S. and Williams, J. L. D. (1981). Descending interneurones in the brain

of the cricket. anatomy and responses to auditory stimuli.Naturwissenschaften,

68(9):486–487.

Boyd, P. and Lewis, B. (1983). Peripheral auditory directionality in the cricket (Gryllus

campestrisL. Teleogryllus oceanicusLe Guillou). Journal of Comparative Physiol-

ogy A: Sensory, Neural, and Behavioral Physiology, 153(4):523–532.

Bradski, G. and Kaehler, A. (2008).Learning OpenCV: Computer Vision with the

OpenCV Library. O’Reilly Media Inc, Sebastopol, CA, USA.

Brunn, D. E. and Dean, J. (1994). Intersegmental and local interneurons in the metatho-

rax of the stick insectCarausius morosusthat monitor middle leg position.Journal

of Neurophysiology, 72(3):1208–1219.

Buchges, A. (1994). The physiology of sensory cells in the ventral scoloparium

of the stick insect femoral chordotonal organ.Journal of Experimental Biology,

189(1):285–292.

Burns, M. D. (1973). The control of walking in orthoptera: I. leg movements in normal

walking. Journal of Experimental Biology, 58(1):45–58.

Bibliography 115

Burrows, M. (1980). The control of sets of motoneurones by local interneurones in the

locust.Journal of Physiology, 298(1):213–233.

Burrows, M. (1987). Parallel processing of proprioceptive signals by spiking local

interneurons and motor neurons in the locust.Journal of Neuroscience, 7(4):1064–

1080.

Burrows, M. (1996a). Components of the nervous system: motoneurons. InThe

neurobiology of an insect brain, pages 48–68. Oxford University Press, Oxford,

UK.

Burrows, M. (1996b).The neurobiology of an insect brain. Oxford University Press,

Oxford, UK.

Buschges, A. (1995). Role of local nonspiking interneurons inthe generation of rhyth-

mic motor activity in the stick insect.Journal of Neurobiology, 27(4):488–512.

Buschges, A., Kittmann, R., and Schmitz, J. (1994). Identifiednonspiking interneu-

rons in leg reflexes and during walking in the stick insect.Journal of Compar-

ative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology,

174(6):685–700.

Buschges, A., Schmitz, J., and Bassler, U. (1995). Rhythmic patterns in the thoracic

nerve cord of the stick insect induced by pilocarpine.Journal of Experimental Biol-

ogy, 198(2):435–456.

Busghes, A. (1990). Nonspiking pathways in a joint-control loop of the stick insect

Carausius Morosus. Journal of Experimental Biology, 151(1):133–160.

Camhi, J. and Johnson, E. (1999). High-frequency steering maneuvers mediated by

tactile cues: antennal wall-following in the cockroach.Journal of Experimental

Biology, 202(5):631–643.

Camhi, J. M. and Levy, A. (1988). Organization of a complex movement: fixed

and variable components of the cockroach escape behavior.Journal of Compar-


163(3):317–328.

Carew, T. (2000).Behavioral neurobiology: the cellular organization of natural be-

havior. Sinauer Associates Publishers, Sunderland, MA, USA.

116 Bibliography

Casaday, G. B. and Hoy, R. R. (1977). Auditory interneurons in thecricketTeleogryllus

oceanicus: Physiological and anatomical properties.Journal of Comparative Physi-

ology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 121(1):1–13.

Cham, J. G., Bailey, S. A., Clark, J. E., Full, R. J., and Cutkosky, M. R. (2002). Fast

and robust: Hexapedal robots via shape deposition manufacturing.The International

Journal of Robotics Research, 21(10-11):869–882.

Choi, J.-u., Rutter, B. L., Kingsley, D. A., Ritzmann, R. E., and Quinn, R. D. (2005).

A robot with cockroach inspired actuation and control. InProceedings, 2005

IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pages

1569–1574. IEEE.

Cohen, M. J. and Jacklet, J. W. (1967). The functional organization of motor neurons

in an insect ganglion.Philosophical Transactions of the Royal Society of London.

Series B, Biological Sciences, 252(781):561 –569.

Comer, C. M. and Dowd, J. P. (1987). Escape turning behavior of the cockroach.Jour-

nal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral

Physiology, 160:571–583.

Cowan, N., Ma, E., Cutkosky, M., and Full, R. (2005). A biologically inspired passive

antenna for steering control of a running robot.Robotics Research, pages 541–550.

Cruse, H. (1976). The function of the legs in the free walking stick insect,Carausius

morosus. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural,


Cruse, H. (1985a). Which parameters control the leg movement of a walking in-

sect?: I. velocity control during the stance phase.Journal of Experimental Biology,

116(1):343–355.

Cruse, H. (1985b). Which parameters control the leg movement of a walking insect?:

II. the start of the swing phase.Journal of Experimental Biology, 116(1):357–362.

Cruse, H. (2002). The functional sense of central oscillations in walking. Biological

Cybernetics, 86:271–280.

Cruse, H. and Bartling, C. (1995). Movement of joint angles in the legs of a walking

insect, carausius morosus.Journal of Insect Physiology, 41(9):761–771.

Bibliography 117

Cruse, H., Bartling, C., Cymbalyuk, G., Dean, J., and Dreifert, M. (1995). A modular

artificial neural net for controlling a six-legged walking system. Biological Cyber-

netics, 72:421–430.

Cruse, H., Dean, J., Muller, U., and Schmitz, J. (1991). The stick insect as a walking

robot. InFifth International Conference on Advanced Robotics, 1991.’Robots in

Unstructured Environments’, 91 ICAR, volume 2, pages 936–940. IEEE.

Cruse, H., Dean, J., and Suilmann, M. (1984). The contributions of diverse sense

organs to the control of leg movement by a walking insect.Journal of Compar-


154(5):695–705.

Cruse, H., Kindermann, T., Schumm, M., Dean, J., and Schmitz,J. (1998). Walknet–

a biologically inspired network to control six-legged walking. Neural Networks,

11(7-8):1435–1447.

Daun-Gruhn, S. and Buschges, A. (2011). From neuron to behavior: dynamic

equation-based prediction of biological processes in motor control. Biological Cy-

bernetics, 105(1):71–88.

Daun-Gruhn, S., Toth, T. I., and Borgmann, A. (2012). Dominance of local sensory

signals over inter-segmental effects in a motor system: modeling studies.Biological

cybernetics, pages 1–14.

Dean, J. (1989). Leg coordination in the stick insectCarausius Morosus: Effects of

cutting thoracic connectives.Journal of Experimental Biology, 145(1):103–131.

Delcomyn, F. (1971). The locomotion of the cockroach periplaneta americana.Journal

of Experimental Biology, 54(2):443–452.

Delcomyn, F. (1980). Neural basis of rhythmic behavior in animals. Science,

210(4469):492–498.

Delcomyn, F. (2004). Insect walking and robotics.Annual Review of Entomology,

49(1):51–70.

Delcomyn, F. and Nelson, M. E. (2000). Architectures for a biomimetic hexapod robot.

Robotics and Autonomous Systems, 30(1-2):5–15.

118 Bibliography

Delcomyn, F. and Usherwood, P. (1973). Motor activity during walking in the cock-

roachPeriplaneta americana. Journal of Experimental Biology, 59(3):629–642.

Doherty, J. A. (1985). Temperature coupling and “trade-off” phenomena in the acous-

tic communication system of the cricket,Gryllus bimaculatusde Geer (Gryllidae).

Journal of Experimental Biology, 114(1):17–35.

Doherty, J. A. (1991). Song recognition and localization inthe phonotaxis behavior of

the field cricket,Gryllus bimaculatus(Orthoptera: gryllidae).Journal of Compar-


168.

Doorly, N. C. (2011). A neuromechanical model for cockroach locomotion. Master’s

thesis, Case Western Reserve University.

Dupuy, F. (2009).La perception des mouvements d’air par le systeme cercal chez le

grillon des bois Nemobius sylvestris. PhD thesis, Universite Francois Rabelais -

Tours.

Durr, V. (2001). Stereotypic leg searching movements in the stick insect: Kinematic

analysis, behavioural context and simulation.Journal of Experimental Biology,

204(9):1589–1604.

Durr, V. (2005). Context-dependent changes in strength and efficacy of leg coordina-

tion mechanisms.Journal of Experimental Biology, 208(12):2253–2267.

Durr, V. and Ebeling, W. (2005). The behavioural transition from straight to curve

walking: kinetics of leg movement parameters and the initiation of turning.Journal


Eibl, E. (1978). Morphology of the sense organs in the proximal parts of the tibiae of

Gryllus campestrisL. andGryllus bimaculatusde Geer (Insecta, Ensifera).Zoomor-

phologie, 89(3):185–205.

Ekeberg, O., Blumel, M., and Buschges, A. (2004). Dynamic simulation of insect

walking. Arthropod Structure & Development, 33(3):287–300.

Epstein, S. and Graham, D. (1983). Behaviour and motor outputof stick insects walk-

ing on a slippery surface: I. forward walking.Journal of Experimental Biology,

105(1):215–229.

Bibliography 119

Espenschied, K. S., Chiel, H. J., Quinn, R. D., and Beer, R. D. (1993). Leg coordina-

tion mechanisms in the stick insect applied to hexapod robotlocomotion.Adaptive

Behavior, 1(4):455–468.

Espenschied, K. S., Quinn, R. D., Beer, R. D., and Chiel, H. J. (1996). Biologically

based distributed control and local reflexes improve rough terrain locomotion in a

hexapod robot.Robotics and Autonomous Systems, 18(1-2):59–64.

Fraenkel, G. S. and Gunn, D. L. (1940).The orientation of animals: Kineses, taxes

and compass reactions. Oxford University Press, Oxford, UK.

Franklin, R., Bell, W. J., and Jander, R. (1981). Rotational locomotion by the cock-

roachBlattella germanica. Journal of Insect Physiology, 27(4):249–255.

Franks, N., Wilby, A., Silverman, B., and Tofts, C. (1992). Self-organizing nest con-

struction in ants: sophisticated building by blind bulldozing. Animal Behaviour, 44,

Part 2(0):357–375.

Frantsevich, L. I. and Mokrushov, P. A. (1980). Turning and righting in Geotrupes

(Coleoptera, Scarabaeidae).Journal of Comparative Physiology A: Neuroethology,

Sensory, Neural, and Behavioral Physiology, 136(4):279–289.

Frik, M., Guddat, M., Losch, D., and Karatas, M. (1998). Terrain adaptive control

of the walking machine Tarry II. InProc. European Mechanics Colloquium, Eu-

romech, volume 375, pages 108–115.

Gassmann, B., Scholl, K. U., and Berns, K. (2001). Locomotion of LAURON III in

rough terrain. In2001 IEEE/ASME International Conference on Advanced Intelli-

gent Mechatronics, 2001. Proceedings, volume 2, pages 959–964. IEEE.

Gebhardt, M. and Honegger, H. (2001). Physiological characterisation of antennal

mechanosensory descending interneurons in an insect (Gryllus bimaculatus, Gryllus

campestris) brain. Journal of Experimental Biology, 204(13):2265–2275.

Gnatzy, W. and Hustert, R. (1989). Mechanoreceptors in Behavior. In Huber, F.,



Graham, D. (1972). A behavioural analysis of the temporal organisation of walking

movements in the 1st instar and adult stick insect (Carausius morosus). Journal of

120 Bibliography

Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Phys-

iology, 81:23–52.

Gruhn, M., Hoffmann, O., Dubbert, M., Scharstein, H., and Buschges, A. (2006).

Tethered stick insect walking: A modified slippery surface setup with optomotor

stimulation and electrical monitoring of tarsal contact.Journal of Neuroscience

Methods, 158(2):195–206.

Gruhn, M., Zehl, L., and Buschges, A. (2009). Straight walking and turning on a

slippery surface.Journal of Experimental Biology, 212(2):194–209.

Hager, W. W. and Zhang, H. (2006). A new active set algorithm for box constrained

optimization.SIAM Journal on Optimization, 17(2):526–557.

Harris, J. and Ghiradella, H. (1980). The forces exerted on the substrate by walking

and stationary crickets.Journal of Experimental Biology, 85(1):263–279.

Hedrick, T. L. (2008). Software techniques for two- and three-dimensional kinematic

measurements of biological and biomimetic systems.Bioinspiration & Biomimetics,

3(3):034001.

Hedwig, B. (1994). A cephalothoracic command system controls stridulation in the

acridid grasshopper omocestus viridulus l.Journal of Neurophysiology, 72(4):2015–

2025.

Hedwig, B. (2000). Control of cricket stridulation by a command neuron: Efficacy

depends on the behavioral state.Journal of Neurophysiology, 83(2):712–722.

Hedwig, B. (2006). Pulses, patterns and paths: neurobiologyof acoustic behaviour

in crickets.Journal of Comparative Physiology A: Neuroethology, Sensory, Neural,


Hedwig, B. and Poulet, J. F. A. (2004). Complex auditory behaviour emerges from

simple reactive steering.Nature, 430(7001):781–5.

Heinrich, R. (2002). Impact of descending brain neurons on the control of stridulation,

walking, and flight in orthoptera.Microscopy Research and Technique, 56(4):292–

301.

Heitler, W. J. and Burrows, M. (1977). The locust jump. i. the motor programme.


Bibliography 121

Hennig, R. M. (1988). Ascending auditory interneurons in thecricket Teleogryllus

commodus(Walker): comparative physiology and direct connections with afferents.

Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behav-

ioral Physiology, 163(1):135–143.

Hennig, R. M. (2003). Acoustic feature extraction by cross-correlation in crickets?

Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behav-

ioral Physiology, 189(8):589–598.

Hess, D. and Buschges, A. (1997). Sensorimotor pathways involved in interjoint reflex

action of an insect leg.Journal of Neurobiology, 33(7):891–913.

Hess, D. and Buschges, A. (1999). Role of proprioceptive signals from an insect femur-

tibia joint in patterning motoneuronal activity of an adjacent leg joint. Journal of

Neurophysiology, 81(4):1856–1865.

Hestenes, M. and Stiefel, E. (1952). Methods of conjugate gradients for solving linear

systems.ournal of Research of the National Bureau of Standards, 49:409–436.

Hofmann, T., Koch, U., and Bassler, U. (1985). Physiology of the femoral chordotonal

organ in the stick insect,Cuniculina impigra. Journal of Experimental Biology,

114(1):207–223.

Horchler, A. D., Reeve, R. E., Webb, B., and Quinn, R. D. (2004). Robot phonotaxis in

the wild: a biologically inspired approach to outdoor soundlocalization.Advanced

Robotics, 18(8):801–816.

Horn, E. and Bischof, H. (1983). Gravity reception in crickets: The influence of cereal

and antennal afferences on the head position.Journal of Comparative Physiology

A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 150(1):93–98.

Horn, E. and Foller, W. (1985). Tonic and modulatory subsystems of the complex grav-

ity receptor system in crickets,Gryllus bimaculatus. Journal of Insect Physiology,

31(12):937–946.

Horseman, G. and Huber, F. (1994). Sound localisation in crickets. i. contralateral

inhibition of an ascending auditory interneuron (AN1) in the cricketGryllus bimac-

ulatus. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and


122 Bibliography

Hoy, R. R. (1978). Acoustic communication in crickets: a modelsystem for the study

of feature detection.Federation Proceedings, 37(10):2316–2323.

Hoy, R. R. and Paul, R. C. (1973). Genetic control of song specificity in crickets.

Science, 180(4081):82–83.

Huber, F. (1960). Untersuchungenuber die funktion des zentralnervensystems und

insbesondere des gehirnes bei der fortbewegung und der lauterzeugung der grillen.

Zeitschrift fur Vergleichende Physiologie, 44(1):60–132.

Huber, F. and Thorson, J. (1985). Cricket auditory communication. Scientific Ameri-

can, 253(6):60–68.

Hughes, G. M. (1952). The Co-Ordination of insect movements ithe walking move-

ments of insects.Journal of Experimental Biology, 29(2):267–285.

Ilg, W. and Berns, K. (1995). A learning architecture based onreinforcement learning

for adaptive control of the walking machine LAURON.Robotics and Autonomous

Systems, 15(4):321–334.

Jindrich, D. and Full, R. (1999). Many-legged maneuverability: dynamics of turning

in hexapods.Journal of Experimental Biology, 202(12):1603–1623.

Kalman, R. E. (1960). A new approach to linear filtering and prediction problems.

Journal of Basic Engineering, 82(1):35–45.

Keller, B. R., Duke, E. R., Amer, A. S., and Zill, S. N. (2007). Tuning posture to body

load: decreases in load produce discrete sensory signals inthe legs of freely stand-

ing cockroaches.Journal of Comparative Physiology A: Neuroethology, Sensory,

Neural, and Behavioral Physiology, 193(8):881–891.

Kennedy, J. S. and Marsh, D. (1974). Pheromone-Regulated anemotaxis in flying

moths.Science, 184(4140):999–1001.

Kien, J. (1983). The initiation and maintenance of walking in the locust: An alternative

to the command concept.Proceedings of the Royal Society of London. Series B.

Biological Sciences, 219(1215):137–174.

Kien, J. (1990a). Neuronal activity during spontaneous walking–I. starting and stop-

ping. Comparative Biochemistry and Physiology Part A: Physiology, 95(4):607–

621.

Bibliography 123

Kien, J. (1990b). Neuronal activity during spontaneous walking–II. correlation

with stepping. Comparative Biochemistry and Physiology Part A: Physiology,

95(4):623–638.

Kostarakos, K., Hennig, M. R., and Romer, H. (2009). Two matched filters and the

evolution of mating signals in four species of cricket.Frontiers in Zoology, 6(1):1–

12.

Kostarakos, K. and Romer, H. (2010). Sound transmission and directional hearing in

field crickets: neurophysiological studies outdoors.Journal of Comparative Physi-

ology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 196:669–681.

Kram, R., Wong, B., and Full, R. J. (1997). Three-dimensional kinematics and

limb kinetic energy of running cockroaches.Journal of Experimental Biology,

200(13):1919–1929.

Kupfermann, I. and Weiss, K. R. (1978). The command neuron concept. Behavioral

and Brain Sciences, 1(1):3–10.

Kutsch, W. and Huber, F. (1989). Neural basis of song production. In Huber, F.,



Laksanacharoen, S., Pollack, A. J., Nelson, G. M., Quinn, R. D., and Ritzmann, R. E.

(2000). Biomechanics and simulation of cricket for microrobot design. InRobotics

and Automation, 2000. Proceedings. ICRA’00. IEEE International Conference on,

volume 2, pages 1088–1094.

Laksanacharoen, S., Quinn, R., and Ritzmann, R. (2003). Modeling of insect’s legs by

inverse kinematics analysis. InProceedings of the 2nd International Symposium on

Adaptive Motion of Animals and Machines, pages 4–8.

Larsen, G., Frazier, S., Fish, S., and Zill, S. (1995). Effects of load inversion in cock-

roach walking.Journal of Comparative Physiology A: Neuroethology, Sensory, Neu-

ral, and Behavioral Physiology, 176(2):229–238.

Larsen, O., Kleindienst, H., and Michelsen, A. (1989). Biophysical aspects of sound

reception. In Huber, F., Moore, T., and Lohrer, W., editors,Cricket Behavior and

Neurobiology, pages 364–390. Cornel University Press, Ithaca, NY, US.

124 Bibliography

Larsen, O. N. (1987). The cricket’s anterior tympanum revisited.Naturwissenschaften,

74(2):92–94.

Laurent, G. and Burrows, M. (1989). Distribution of intersegmental inputs to nonspik-

ing local interneurons and motor neurons in the locust.Journal of Neuroscience,

9(9):3019–3029.

Laurent, G. and Richard, D. (1986a). The organization and role during locomotion

of the proximal musculature of the cricket foreleg: I. Anatomy and innervation.


Laurent, G. and Richard, D. (1986b). The organization and role during locomotion

of the proximal musculature of the cricket foreleg: II. Electromyographic activity

during stepping patterns.Journal of Experimental Biology, 123(1):285–306.

Lewinger, W., Rutter, B., Blumel, M., Buschges, A., and Quinn, R. (2006). Sen-

sory coupled action switching modules (scasm) generate robust, adaptive stepping

in legged robots. InInternational Conference on Climbing and Walking Robots,

volume 2006, pages 661–671, Brussels, Belgium.

Lewinger, W. A. and Quinn, R. D. (2008). BILL-LEGS: low computation emergent

gait system for small mobile robots. InIEEE International Conference on Robotics

and Automation, 2008. ICRA 2008, pages 251–256. IEEE.

Lewinger, W. A. and Quinn, R. D. (2009). A small, autonomous, agile robot with

an on-board, neurobiologically-based control system. InIEEE/RSJ International

Conference on Intelligent Robots and Systems, 2009. IROS 2009, pages 412–413.

IEEE.

Loher, W., Weber, T., and Huber, F. (1993). The effect of mating on phonotactic

behaviour inGryllus bimaculatus(de Geer).Physiological Entomology, 18(1):57–

66.

Ludwar, B., Goritz, M. L., and Schmidt, J. (2005). Intersegmental coordination of

walking movements in stick insects.Journal of Neurophysiology, 93(3):1255–1265.

Lund, H. H., Webb, B., and Hallam, J. (1997). A robot attractedto the cricket species

Gryllus bimaculatus. In Proceedings of the Fourth European Conference on Artifi-

cial Life, pages 131–140.

Bibliography 125

Mason, A. C., Lee, N., and Oshinsky, M. L. (2005). The start of phonotactic walking

in the fly Ormia ochracea: a kinematic study.Journal of Experimental Biology,

208(24):4699–4708.

Matsumoto, Y. and Mizunami, M. (2002). Temporal determinants of long-term reten-

tion of olfactory memory in the cricketGryllus bimaculatus. Journal of Experimen-

tal Biology, 205(10):1429–1437.

Maye, A., Hsieh, C.-h., Sugihara, G., and Brembs, B. (2007). Order in spontaneous

behavior.PLoS ONE, 2(5):e443.

Mhatre, N. and Balakrishnan, R. (2007). Phonotactic walking paths of field crickets

in closed-loop conditions and their simulation using a stochastic model.Journal of


Michel, K. (1974). Das tympanalorgan vonGryllus bimaculatusdegeer (saltatoria,

gryllidae). Zeitschrift fur Morphologie der Tiere, 77:285–315.

Michelsen, A., Popov, A., and Lewis, B. (1994). Physics of directional hearing in the

cricketGryllus bimaculatus. Journal of Comparative Physiology A: Neuroethology,

Sensory, Neural, and Behavioral Physiology, 175(2):153–164.

Mu, L. and Ritzmann, R. E. (2005). Kinematics and motor activity during tethered

walking and turning in the cockroach,Blaberus discoidalis. Journal of Compar-


191(11):1037–1054.

Murphey, R. K. and Zaretsky, M. D. (1972). Orientation to calling song by female

crickets,Scapsipedus marginatus(Gryllidae). Journal of Experimental Biology,

56(2):335–352.

Nabatiyan, A., Poulet, J., de Polavieja, G. G., and Hedwig, B.(2003). Temporal pattern

recognition based on instantaneous spike rate coding in a simple auditory system.


Nishino, H. (2000). Topographic mapping of the axons of the femoral chordo-

tonal organ neurons in the cricketGryllus bimaculatus. Cell and Tissue Research,

299(1):145–157.

126 Bibliography

Nishino, H. (2003). Local innervation patterns of the metathoracic flexor and exten-

sor tibiae motor neurons in the cricketGryllus bimaculatus. Zoological Science,

20(6):697–707.

Nishino, H. and Sakai, M. (1997). Three neural groups in the femoral chordotonal

organ of the cricketGryllus bimaculatus: central projections and soma arrange-

ment and displacement during joint flexion.Journal of Experimental Biology,

200(20):2583–2595.

Nowel, M., Shelton, P., and Stephen, R. (1995). Functional organisation of the

metathoracic femoral chordotonal organ in the cricketAcheta domesticus. Journal


Nye, S. W. and Ritzmann, R. E. (1992). Motion analysis of leg joints associated

with escape turns of the cockroach,Periplaneta americana. Journal of Compar-


171(2):183–194.

Olberg, R., Worthington, A., and Venator, K. (2000). Prey pursuit and interception in

dragonflies.Journal of Comparative Physiology A: Neuroethology, Sensory, Neural,


Payne, M. (2010).Co-ordinating Behaviours in an Insect Biorobot. PhD thesis, Uni-

versity of Edinburgh.

Payne, M., Hedwig, B., and Webb, B. (2010). Multimodal predictive control in crick-

ets. In Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, J.-A., and Mouret,

J.-B., editors,From Animals to Animats 11, volume 6226 ofLecture Notes in Com-

puter Science, pages 167–177. Springer Berlin / Heidelberg.

Pearson, K. G. (1972). Central programming and reflex controlof walking in the

cockroach.Journal of Experimental Biology, 56(1):173–193.

Pearson, K. G. and Fourtner, C. R. (1975). Nonspiking interneurons in walking system

of the cockroach.Journal of Neurophysiology, 38(1):33–52.

Pearson, K. G. and Iles, J. F. (1973). Nervous mechanisms underlying intersegmen-

tal Co-Ordination of leg movements during walking in the cockroach. Journal of


Bibliography 127

Petrou, G. and Webb, B. (2012). Detailed tracking of body and leg movements of a

freely walking female cricket during phonotaxis.Journal of Neuroscience Methods,

203(1):56–68.

Pfeiffer, F., Eltze, J., and Weidemann, H. (1995). Six-legged technical walking consid-

ering biological principles.Robotics and Autonomous Systems, 14(2-3):223–232.

Pollack, G. S. and Hoy, R. R. (1979). Temporal pattern as a cue for species-specific

calling song recognition in crickets.Science, 204(4391):429–432.

Popov, A. V. and Markovich, A. M. (1982). Auditory interneurones in the prothoracic

ganglion of the cricket,Gryllus bimaculatus. Journal of Comparative Physiology A:

Neuroethology, Sensory, Neural, and Behavioral Physiology, 146(3):351–359.

Popov, A. V., Markovich, A. M., and Andjan, A. S. (1978). Auditory interneurons

in the prothoracic ganglion of the cricket,Gryllus bimaculatusDeGeer.Journal of


iology, 126(2):183–192.

Popov, A. V. and Shuvalov, V. F. (1977). Phonotactic behavior of crickets.Journal of


iology, 119(1):111–126.

Poulet, J. F. A. and Hedwig, B. (2005). Auditory orientation in crickets: Pattern recog-

nition controls reactive steering.Proceedings of the National Academy of Sciences

of the United States of America, 102(43):15665–15669.

Quinn, R. and Ritzmann, R. (1998). Construction of a hexapod robot with cockroach

kinematics benefits both robotics and biology.Connection Science, 10(3):239–254.

Reeve, R., Schaik, A. v., Jin, C., Hamilton, T., Torben-Nielsen, B., and Webb, B.

(2007). Directional hearing in a silicon cricket.BioSystems, 87(2-3):307–313.

Reeve, R. E. and Webb, B. H. (2003). New neural circuits for robotphonotaxis.Philo-

sophical Transactions of the Royal Society of London A, 361(1811):2245–2266.

Regen, J. (1913).Ueber die anlockung des weibchens vonGryllus campestrisdurch

telephonischeubertragung der stridulation des mannchens.Pflugers Archiv Euro-

pean Journal of Physiology, 155(1):193–200.

128 Bibliography

Rheinlaender, J. and Blatgen, G. (1982). The precision of auditory lateralizationin the

cricket,Gryllus bimaculatus. Physiological Entomology, 7(2):209–218.

Ridgel, A., Alexander, B., and Ritzmann, R. (2007). Descending control of turning

behavior in the cockroach,Blaberus discoidalis. Journal of Comparative Physiology


Romer, H. (1993). Environmental and biological constraintsfor the evolution of Long-

Range signalling and hearing in acoustic insects.Royal Society of London Philo-

sophical Transactions Series B, 340:179–185.

Rosano, H. and Webb, B. (2007). A dynamic model of thoracic differentiation for the

control of turning in the stick insect.Biological Cybernetics, 97(3):229–246.

Rutter, B., Taylor, B., Bender, J., Blumel, M., Lewinger, W., Ritzmann, R., and Quinn,

R. (2011). Descending commands to an insect leg controller network cause smooth

behavioral transitions. InIntelligent Robots and Systems (IROS), 2011 IEEE/RSJ

International Conference on, pages 215–220.

Ryckebusch, S. and Laurent, G. (1993). Rhythmic patterns evoked in locust leg mo-

tor neurons by the muscarinic agonist pilocarpine.Journal of Neurophysiology,

69(5):1583–1595.

Saranli, U., Buehler, M., and Koditschek, D. E. (2001). RHex: asimple and highly

mobile hexapod robot.The International Journal of Robotics Research, 20(7):616–

631.

Schildberger, K. (1984a). Multimodal interneurons in the cricket brain: properties of

identified extrinsic mushroom body cells.Journal of Comparative Physiology A:


Schildberger, K. (1984b). Temporal selectivity of identified auditory neurons in the

cricket brain.Journal of Comparative Physiology A: Neuroethology, Sensory, Neu-

ral, and Behavioral Physiology, 155(2):171–185.

Schildberger, K. (1988). Behavioral and neuronal mechanisms of cricket phonotaxis.

Cellular and Molecular Life Sciences (CMLS), 44(5):408–415.

Bibliography 129

Schildberger, K. and Horner, M. (1988). The function of auditory neurons in cricket

phonotaxis. I. influence of hyperpolarization of identifiedneurons on sound local-

ization.Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and

Behavioral Physiology, 163:621–631.

Schildberger, K., Huber, F., and Wohlers, D. W. (1989). Central auditory pathway:

neuronal correlates of phonotactic behavior. In Huber, F.,Moore, T., and Lohrer,

W., editors,Cricket Behavior and Neurobiology, pages 423–458. Cornel University

Press, Ithaca, NY, US.

Schilling, M., Cruse, H., and Arena, P. (2007). Hexapod walking: an expansion to

walknet dealing with leg amputations and force oscillations.Biological Cybernetics,

96:323–340.

Schmitz, B., Scharstein, H., and Wendler, G. (1982). Phonotaxis in Gryllus campestris

l. (orthoptera, gryllidae).Journal of Comparative Physiology A: Neuroethology,

Sensory, Neural, and Behavioral Physiology, 148:431–444.

Schneider, A., Cruse, H., and Schmitz, J. (2005). A biologically inspired active com-

pliant joint using local positive velocity feedback (LPVF). IEEE Transactions on

Systems, Man, and Cybernetics, Part B: Cybernetics, 35(6):1120–1130.

Schneider, A., Cruse, H., and Schmitz, J. (2006). Decentralized control of elastic

limbs in closed kinematic chains.The International Journal of Robotics Research,

25(9):913–930.

Schoneich, S. and Hedwig, B. (2010). Hyperacute directional hearing and phonotactic

steering in the cricket (Gryllus bimaculatusde geer).PLoS ONE, 5(12):e15141.

Selverston, A., Kleindienst, H., and Huber, F. (1985). Synaptic connectivity between

cricket auditory interneurons as studied by selective photoinactivation. Journal of

Neuroscience, 5(5):1283–1292.

Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio,M., Moore, R., Kipman,

A., and Blake, A. (2011). Real-time human pose recognition in parts from single

depth images. InCVPR, volume 2, page 3.

Siegler, M. V. (1981). Postural changes alter synaptic interactions between nonspik-

ing interneurons and motor neurons of the locust.Journal of Neurophysiology,

46(2):310–323.

130 Bibliography

Siegler, M. V. S. and Pousman, C. A. (1990). Distribution of motor neurons into

anatomical groups in the grasshopper metathoracic ganglion. Journal of Compara-

tive Neurology, 297(2):313–327.

Simmons, L. (1990). Post-copulatory guarding, female choice and the levels of gre-

garine infections in the field cricket,Gryllus bimaculatus. Behavioral Ecology and

Sociobiology, 26:403–407.

Sponberg, S. and Full, R. (2008). Neuromechanical response of musculo-skeletal

structures in cockroaches during rapid running on rough terrain. Journal of Ex-

perimental Biology, 211(3):433–446.

Srinivasan, M., Zhang, S., Altwein, M., and Tautz, J. (2000). Honeybee navigation:

nature and calibration of the “odometer”.Science, 287(5454):851–853.

Stabel, J., Wendler, G., and Scharstein, H. (1989). Cricket phonotaxis: localization de-

pends on recognition of the calling song pattern.Journal of Comparative Physiology


Staudacher, E. (1998). Distribution and morphology of descending brain neurons in

the cricketGryllus bimaculatus. Cell and Tissue Research, 294(1):187–202.

Staudacher, E. and Schildberger, K. (1998). Gating of sensory responses of descend-

ing brain neurones during walking in crickets.Journal of Experimental Biology,

201(4):559–572.

Staudacher, E. M. (2001). Sensory responses of descending brain neurons in the walk-

ing cricket,Gryllus bimaculatus. Journal of Comparative Physiology A: Neuroethol-

ogy, Sensory, Neural, and Behavioral Physiology, 187(1):1–17.

Stewart, F. J., Baker, D. A., and Webb, B. (2010). A model of visual-olfactory integra-

tion for odour localisation in free-flying fruit flies.Journal of Experimental Biology,

213(11):1886–1900.

Stout, J. F., DeHaan, C. H., and McGhee, R. W. (1983). Attractiveness of the male

Acheta domesticuscalling song to females. i. dependence on each of the callingsong

features. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural,


Bibliography 131

Stout, J. F. and McGhee, R. W. (1988). Attractiveness of the male Acheta domesticus

calling song to females. II. The relative importance of syllable period, intensity and

chirp rate.Journal of Comparative Physiology A: Neuroethology, Sensory, Neural,


Strauss, R. and Heisenberg, M. (1990). Coordination of legs during straight walking

and turning inDrosophila melanogaster. Journal of Comparative Physiology A:


Takeuchi, S. and Shimoyama, I. (2004). A radio-telemetry system with a shape mem-

ory alloy microelectrode for neural recording of freely moving insects.IEEE Trans-

actions on Biomedical Engineering, 51(1):133–137.

Thorson, J., Weber, T., and Huber, F. (1982). Auditory behavior of the cricket. II. Sim-

plicity if calling song recognition inGryllus, and anomalous phonotaxis at abnormal

carrier frequencies.Journal of Comparative Physiology A: Neuroethology, Sensory,

Neural, and Behavioral Physiology, 146:361–378.

Tryba, A. K. and Ritzmann, R. E. (2000). Multi-Joint coordination during walking and

foothold searching in theBlaberuscockroach. I. kinematics and electromyograms.


Von Frisch, K. (1967).The dance language and orientation of bees. Harvard University

Press, Cambridge, MA, US.

von Twickel, A., Buschges, A., and Pasemann, F. (2011). Deriving neural network

controllers from neuro-biological data: implementation of a single-leg stick insect

controller.Biological Cybernetics, 104(1-2):95–119.

von Twickel, A., Hild, M., Siedel, T., Patel, V., and Pasemann, F. (2012). Neural con-

trol of a modular multi-legged walking machine: Simulationand hardware.Robotics

and Autonomous Systems, 60(2):227–241.

Walker, T. J. and Masaki, S. (1989). Natural history. In Huber, F., Moore, T., and

Lohrer, W., editors,Cricket Behavior and Neurobiology, pages 1–42. Cornel Uni-

versity Press, Ithaca, NY, US.

Watson, A. H. D., Burrows, M., and Hale, J. P. (1985). The morphology and ultra-

structure of common inhibitory motor neurones in the thoraxof the locust.Journal

of Comparative Neurology, 239(3):341–359.

132 Bibliography

Watson, J. T. and Ritzmann, R. E. (1997). Leg kinematics and muscle activity during

treadmill running in the cockroach,Blaberus discoidalis: I. slow running. Jour-


Physiology, 182(1):11–22.

Watson, J. T., Ritzmann, R. E., Zill, S. N., and Pollack, A. J. (2002). Control of ob-

stacle climbing in the cockroach,Blaberus discoidalis. I. Kinematics. Journal of


iology, 188(1):39–53.

Webb, B. (1995). Using robots to model animals: a cricket test. Robotics and Au-

tonomous Systems, 16(2-4):117–134.

Webb, B. (2000). What does robotics offer animal behaviour?Animal Behaviour,

60(5):545–558.

Webb, B. (2001). Can robots make good models of biological behaviour? Behavioral

and brain sciences, 24:1033–1050.

Webb, B. (2006). Validating biorobotic models.Journal of Neural Engineering,

3(3):R25–R35.

Webb, B. (2008). Spiking neuron controllers for a sound localising robot. In Arena, P.,

Pfeiffer, F., Rammerstorfer, F. G., Salenon, J., Schrefler, B., and Serafini, P., editors,

Dynamical Systems, Wave-Based Computation and Neuro-Inspired Robots, volume

500 ofCISM Courses and Lectures, pages 119–130. Springer Vienna.

Webb, B. and Scutt, T. (2000). A simple latency-dependent spiking-neuron model of

cricket phonotaxis.Biological Cybernetics, 82(3):247–269.

Weber, T. and Thorson, J. (1989). Phonotactic behavior of walking crickets. In Huber,

F., Moore, T., and Lohrer, W., editors,Cricket Behavior and Neurobiology, pages

310–339. Cornel University Press, Ithaca, NY, USA.

Weber, T., Thorson, J., and Huber, F. (1981). Auditory behavior of the cricket. I. Dy-

namics of compensated walking and discrimination paradigms on the kramer tread-

mill. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and


Bibliography 133

Wehner, R. (2003). Desert ant navigation: how miniature brains solve complex tasks.

Journal of Comparative Physiology A: Sensory, Neural, and Behavioral Physiology,

189:579–588.

Weidemann, H. J., Pfeiffer, F., and Eltze, J. (1993). A design concept for legged robots

derived from the walking stick insect. InProceedings of the 1993 IEEE/RSJ Inter-

national Conference on Intelligent Robots and Systems ’93, IROS ’93, volume 1,

pages 545–552. IEEE.

Weidemann, H. J., Pfeiffer, F., and Eltze, J. (1994). The six-legged TUM walking

robot. InProceedings of the IEEE/RSJ/GI International Conference onIntelligent

Robots and Systems ’94. ’Advanced Robotic Systems and the Real World’, IROS ’94,

volume 2, pages 1026–1033. IEEE.

Wendler, G. (1990). Pattern recognition and localization in cricket phonotaxis. In Grib-

akin, G., Wiese, K., and Popov, A., editors,Sensory systems and communication in

arthropods, pages 387–394. Basel: Birkhauser.

Wiese, K. (1981). Influence of vibration on cricket hearing:Interaction of low fre-

quency vibration and acoustic stimuli in the omega neuron.Journal of Compar-


143(1):135–142.

Wiese, K. and Eilts, K. (1985). Evidence for matched frequency dependence of bi-

lateral inhibition in the auditory pathway ofGryllus bimaculatus. Zoologische

Jahrbucher, 89(2):181–201.

Witney, A. G. and Hedwig, B. (2011). Kinematics of phonotactic steering in the

walking cricketGryllus bimaculatus(de Geer).Journal of Experimental Biology,

214(1):69–79.

Wohlers, D. W. and Huber, F. (1978). Intracellular recording and staining of cricket

auditory interneuronsGryllus campestrisL., Gryllus bimaculatusDeGeer). Jour-


Physiology, 127(1):11–28.

Wohlers, D. W. and Huber, F. (1982). Processing of sound signals by six types of

neurons in the prothoracic ganglion of the cricketGryllus campestrisL. Journal of

134 Bibliography


iology, 146(2):161–173.

Wolf, H. and Laurent, G. (1994). Rhythmic modulation of the responsiveness of lo-

cust sensory local interneurons by walking pattern generating networks.Journal of

Neurophysiology, 71(1):110–118.

Yoneda, K. and Ota, Y. (2003). Non-Bio-Mimetic walkers.The International Journal

of Robotics Research, 22(3-4):241–249.

Zakotnik, J., Matheson, T., and Durr, V. (2004). A posture optimization algorithm

for model-based motion capture of movement sequences.Journal of Neuroscience

Methods, 135(1-2):43–54.

Zhang, Y., Huang, H., Liu, X., and Ren, L. (2011). Kinematics of terrestrial locomotion

in mole cricketGryllotalpa orientalis. Journal of Bionic Engineering, 8(2):151–157.

Zill, S. N. (1986). A model of pattern generation of cockroach walking reconsidered.

Journal of Neurobiology, 17(4):317–328.

Zollikofer, C. P. E. (1994). Stepping patterns in ants. I. Influence of speed and curva-

ture. Journal of Experimental Biology, 192(1):95–106.

Zolotov, V., Frantsevich, L., and Falk, E. M. (1975). Kinematik der phototaktischen

drehung bei der honigbieneApis melliferaL. Journal of Comparative Physiology


Zorovic, M. and Hedwig, B. (2011). Processing of species-specific auditory patterns

in the cricket brain by ascending, local, and descending neurons during standing and

walking. Journal of Neurophysiology, 105(5):2181–2194.

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